Restructuring
This commit is contained in:
+3
-145
@@ -1,147 +1,5 @@
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# ---> Python
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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cover/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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||||
.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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.pybuilder/
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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# For a library or package, you might want to ignore these files since the code is
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# intended to run in multiple environments; otherwise, check them in:
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# .python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# pytype static type analyzer
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.pytype/
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# Cython debug symbols
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cython_debug/
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.idea
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working/tuner
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working
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.idea
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__pycache__
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figures
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baseline_results.json
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baseline_results_v3.json
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results
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working
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BIN
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{"strategy":{"0":"most_frequent","1":"stratified","2":"uniform","3":"most_frequent","4":"stratified","5":"uniform","6":"most_frequent","7":"stratified","8":"uniform","9":"most_frequent","10":"stratified","11":"uniform","12":"most_frequent","13":"stratified","14":"uniform","15":"most_frequent","16":"stratified","17":"uniform"},"time used":{"0":"1HR","1":"1HR","2":"1HR","3":"1HR","4":"1HR","5":"1HR","6":"1HR","7":"1HR","8":"1HR","9":"1HR","10":"1HR","11":"1HR","12":"1HR","13":"1HR","14":"1HR","15":"1HR","16":"1HR","17":"1HR"},"sequence length":{"0":1,"1":1,"2":1,"3":6,"4":6,"5":6,"6":11,"7":11,"8":11,"9":16,"10":16,"11":16,"12":21,"13":21,"14":21,"15":26,"16":26,"17":26},"accuracy":{"0":0.0465710356,"1":0.0387109452,"2":0.0322263706,"3":0.0471161657,"4":0.0337124289,"5":0.0314784728,"6":0.0476990964,"7":0.0355116621,"8":0.0310989704,"9":0.0483239007,"10":0.035263387,"11":0.0343926861,"12":0.0489952585,"13":0.0363513208,"14":0.0325129826,"15":0.0497185741,"16":0.0356472795,"17":0.0300187617},"precision":{"0":0.0021688614,"1":0.038723827,"2":0.035357886,"3":0.0022199331,"4":0.0344564588,"5":0.0368125232,"6":0.0022752038,"7":0.0355258063,"8":0.034249105,"9":0.0023351994,"10":0.0353594615,"11":0.038321258,"12":0.0024005354,"13":0.0363413163,"14":0.0372264035,"15":0.0024719366,"16":0.0354808739,"17":0.0381814576},"recall":{"0":0.0465710356,"1":0.0387109452,"2":0.0322263706,"3":0.0471161657,"4":0.0337124289,"5":0.0314784728,"6":0.0476990964,"7":0.0355116621,"8":0.0310989704,"9":0.0483239007,"10":0.035263387,"11":0.0343926861,"12":0.0489952585,"13":0.0363513208,"14":0.0325129826,"15":0.0497185741,"16":0.0356472795,"17":0.0300187617},"f1 score":{"0":0.0041446997,"1":0.0386459741,"2":0.0329666692,"3":0.0042400894,"4":0.0340329362,"5":0.033074915,"6":0.004343239,"7":0.0354644534,"8":0.0315466073,"9":0.0044551105,"10":0.0352542225,"11":0.0349449461,"12":0.0045768279,"13":0.0362991035,"14":0.0333112368,"15":0.004709713,"16":0.0355062319,"17":0.0327189637}}
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@@ -1,29 +1,23 @@
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import json
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import os
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import math
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import numpy as np
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import pandas as pd
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import sklearn
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from keras.src.regularizers import L1L2
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from matplotlib import pyplot as plt
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from pandas import DataFrame
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from sklearn.dummy import DummyClassifier
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from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, GradientBoostingClassifier
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from sklearn.model_selection import GridSearchCV
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from sklearn.naive_bayes import BernoulliNB
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from sklearn.preprocessing import MinMaxScaler
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from pipeline import (
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load_dataset,
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filter_data,
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filter_test_data,
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prepare_user_data,
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train_models,
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evaluate_models,
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prepare_data_for_model, model_type_gru, model_type_lstm, model_type_bilstm, train_models_v2, train_one_model,
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eval_metrics, get_save_id
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prepare_data_for_neural_model, model_type_gru, model_type_lstm, model_type_bilstm,
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eval_metrics, prepare_data_for_basic_algorithm, train_one_model,
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)
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year_str = 'Year'
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month_str = 'Month'
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day_str = 'Day'
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date_str = 'Date'
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time_str = 'Time'
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day_of_week_str = 'DayOfWeek'
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@@ -31,9 +25,6 @@ user_str = 'user'
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split_str = 'split type'
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data_split_str = 'data percentages'
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month_split_str = 'month percentages'
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threshold_str = 'threshold used'
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with_threshold_str = 'WITH'
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without_threshold_str = 'WITHOUT'
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timespan_str = 'time used'
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hour_timespan_str = '1HR'
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min_timespan_str = '15MIN'
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@@ -46,30 +37,11 @@ model_type_str = 'model type'
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week_column_names = ['DayOfWeek_' + day for day in
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['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday' ]]
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figure_path = 'figures/'
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predicitons_path = 'preds/'
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# === Configurable Parameters ===
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dataset_path = './Datasets/'
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dataset_hrs_path = './Datasets/hours.json'
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dataset_min_path = './Datasets/minutes.json'
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DATA_PATH = dataset_path +'ALLUSERS32_15MIN_WITHOUTTHREHOLD.xlsx'
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OUTPUT_EXCEL_PATH = './working/evaluation_results.xlsx'
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result_filename_v1 = './working/evaluation_results.json'
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result_filename_v2 = './working/evaluation_results_v2.json'
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SEQUENCE_LENGTHS = [30, 25, 20, 15, 10, 5] # You can add more: [20, 25, 30]
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TRAINING_SCENARIO = [(2018, list(range(1, 13))), (2019, list(range(1, 10)))]
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VALIDATION_SCENARIO = [(2019, [10, 11, 12])]
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TEST_SCENARIO = [(2020, [1, 2])] # Jan–Feb 2020 only
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# === Optional display only ===
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predefined_training_scenarios = {
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"Scenario 1": {"years_months": [(2018, list(range(1, 13))), (2019, list(range(1, 10)))]},
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"Scenario 2": {"years_months": [(2017, list(range(1, 13))), (2018, list(range(1, 13))), (2019, list(range(1, 10)))]}
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}
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predefined_validation_scenarios = {
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"Scenario A": {"years_months": [(2019, [10, 11, 12])]}
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}
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def create_dir(path):
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"""
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@@ -81,424 +53,74 @@ def create_dir(path):
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os.makedirs(path)
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def remove_covid_data(df):
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"""
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Removes covid data from dataframe because the steps from these times will most likely differ from before
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:param df: Dataframe with the data
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:return: the data without covid time data
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"""
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df = df[~(df[year_str]>=2020)]
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return df
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def split_data_by_month_percentage(df, percentages):
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train_p, valid_p, test_p = percentages
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ids = df[[year_str, month_str]].drop_duplicates().sort_values([year_str, month_str])
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tr, va, te = np.split(ids, [int((train_p/100) * len(ids)), int(((train_p + valid_p)/100) * len(ids))])
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return df.merge(tr, on=[year_str, month_str], how='inner'), df.merge(va, on=[year_str, month_str], how='inner'), df.merge(te, on=[year_str, month_str], how='inner')
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def split_data_by_userdata_percentage(df, percentages, sample=100):
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"""
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Splits data by userdata percentages. Each users data will be split according to the given percentages along the time axis
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:param df: Data with all users data
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:param percentages: triple with percentages
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:param sample: overall percentage if less data should be used. Use only for testing. Has an error!!
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:return: the split data
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"""
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train_p, valid_p, test_p = percentages
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tr, va, te = pd.DataFrame(), pd.DataFrame(), pd.DataFrame()
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for user_id in df[user_str].unique():
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user_data = df[df[user_str]==user_id].sample(frac=sample/ 100).sort_values([year_str, month_str])
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# !! following sample creates gaps in data if sample smaller 100
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user_data = df[df[user_str]==user_id].sample(frac=sample/ 100).sort_values([date_str]) # have to sort for time shift
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u_tr, u_va, u_te = np.split(user_data, [int((train_p/100)*len(user_data)), int(((train_p+valid_p)/100)*len(user_data))])
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tr = pd.concat([tr, u_tr], ignore_index=True)
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va = pd.concat([va, u_va], ignore_index=True)
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te = pd.concat([te, u_te], ignore_index=True)
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return tr, va, te
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def main():
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# print("=== Training Scenario Setup ===")
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# display_warning_about_2020_data()
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# display_warnings_for_scenarios("training", predefined_training_scenarios, predefined_validation_scenarios)
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# print("\n=== Validation Scenario Setup ===")
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# display_warning_about_2020_data()
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# display_warnings_for_scenarios("validation", predefined_training_scenarios, predefined_validation_scenarios)
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# === Load and preprocess ===
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df = load_dataset(DATA_PATH)
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ALLUSERS32_15MIN_WITHOUTTHREHOLD = False
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if('ALLUSERS32_15MIN_WITHOUTTHREHOLD.xlsx' in DATA_PATH):
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ALLUSERS32_15MIN_WITHOUTTHREHOLD = True
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training_data = filter_data(df, TRAINING_SCENARIO, ALLUSERS32_15MIN_WITHOUTTHREHOLD)
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validation_data = filter_data(df, VALIDATION_SCENARIO, ALLUSERS32_15MIN_WITHOUTTHREHOLD)
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user_data_train = prepare_user_data(training_data)
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user_data_val = prepare_user_data(validation_data)
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# === Train models ===
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best_models = train_models(user_data_train, user_data_val, sequence_lengths=SEQUENCE_LENGTHS)
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# === Load and evaluate test ===
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test_df = filter_test_data(df, TEST_SCENARIO)
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evaluate_models(best_models, test_df, SEQUENCE_LENGTHS, OUTPUT_EXCEL_PATH, ALLUSERS32_15MIN_WITHOUTTHREHOLD)
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print(f"\n✅ All evaluations completed. Results saved to: {OUTPUT_EXCEL_PATH}")
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def reduce_columns(df, filename):
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if min_timespan_str in filename:
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return df.drop(columns=['Month', 'Year', 'date', 'DayOfWeek'] + week_column_names, errors='ignore')
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else:
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return df.drop(columns=['Month', 'Year', 'date', 'DayOfWeek'], errors='ignore')
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def reduce_columns_v3(df):
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def reduce_columns(df):
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"""
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Removes unnecessary columns from dataframe.
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:param df: Dataframe with the data
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:return: dataframe without unnecessary columns
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"""
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return df.drop(columns=[month_str, year_str, date_str])
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def load_previous_results(filename):
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results = pd.DataFrame()
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if os.path.exists(filename):
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results = pd.DataFrame(json.load(open(filename)))
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return results
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def filter_and_preprocess_data(filename, sample=100, print_unique=False):
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"""
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Preprocesses data. Removes users with too little data or which subsume another, bins the step data and creates train, valid, test splits
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def main_two_v2(model_type):
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seq_length = range(10,31, 5)
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for sequence_length in seq_length:
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for data_filename in os.listdir(dataset_path):
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timespan_id = hour_timespan_str
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threshold_id = with_threshold_str
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if min_timespan_str in data_filename:
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timespan_id = min_timespan_str
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if without_threshold_str in data_filename:
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threshold_id = without_threshold_str
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results = load_previous_results(result_filename_v2)
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if len(results) > 0:
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if len(results[(results[timespan_str]==timespan_id) &
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(results[threshold_str]==threshold_id) &
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(results[sequence_length_str]==sequence_length) &
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(results[model_type_str]==model_type)]) > 0:
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continue
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file_path = os.path.join(dataset_path, data_filename)
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df = load_dataset(file_path)
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df = remove_covid_data(df)
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tr,val,te = split_data_by_userdata_percentage(df, percentages=(80,10,10))
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tr = reduce_columns(tr, data_filename)
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val = reduce_columns(val, data_filename)
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te = reduce_columns(te, data_filename)
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user_data_train = prepare_user_data(tr)
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user_data_val = prepare_user_data(val)
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best_model = train_models_v2(user_data_train, user_data_val,
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sequence_length=sequence_length,
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model_type=model_type)
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results = load_previous_results(result_filename_v2)
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results = pd.concat([results,
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evaluate_model_on_test_data(model=best_model,
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test_df=te,
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sequence_length=sequence_length,
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time_span_id=timespan_id,
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threshold_id=threshold_id,
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model_type=model_type,
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split_id=data_split_str)],
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ignore_index=True)
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results.to_json(result_filename_v2)
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def main_two_v1():
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seq_length = [30, 25, 20, 15, 10, 5] # You can add more: [20, 25, 30]
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results = pd.DataFrame()
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if os.path.exists(result_filename_v1):
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results = pd.DataFrame(json.load(open(result_filename_v1)))
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for sequence_length in seq_length:
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for data_filename in os.listdir(dataset_path):
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for split_id, split_method in [(data_split_str, split_data_by_userdata_percentage),(month_split_str, split_data_by_month_percentage)]:
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for model_type in [model_type_lstm, model_type_bilstm, model_type_gru]:
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timespan_id = hour_timespan_str
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threshold_id = with_threshold_str
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if min_timespan_str in data_filename:
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timespan_id = min_timespan_str
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if without_threshold_str in data_filename:
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threshold_id = without_threshold_str
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if len(results) > 0:
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if len(results[(results[split_str]==split_id) &
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(results[timespan_str]==timespan_id) &
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(results[threshold_str]==threshold_id) &
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(results[sequence_length_str]==sequence_length) &
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(results[model_type_str]==model_type)]) > 0:
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continue
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file_path = os.path.join(dataset_path, data_filename)
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df = load_dataset(file_path)
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df = remove_covid_data(df)
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tr,val,te = split_method(df, percentages=(80,10,10))
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tr = reduce_columns(tr, data_filename)
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val = reduce_columns(val, data_filename)
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te = reduce_columns(te, data_filename)
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user_data_train = prepare_user_data(tr)
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||||
user_data_val = prepare_user_data(val)
|
||||
|
||||
best_models = train_models(user_data_train, user_data_val, sequence_lengths=[sequence_length], model_type=model_type)
|
||||
|
||||
results = pd.concat([results,
|
||||
evaluate_model_on_test_data(model=best_models[sequence_length]['model'],
|
||||
test_df=te, split_id=split_id,
|
||||
sequence_length=sequence_length,
|
||||
time_span_id=timespan_id,
|
||||
threshold_id=threshold_id,
|
||||
model_type=model_type)], ignore_index=True)
|
||||
results.to_json(result_filename_v1)
|
||||
|
||||
# === Evaluation ===
|
||||
def evaluate_model_on_test_data(model, test_df,sequence_length, split_id, threshold_id, time_span_id, model_type):
|
||||
user_data = prepare_user_data(test_df)
|
||||
x, y = prepare_data_for_model(user_data=user_data, sequence_length=sequence_length)
|
||||
|
||||
y_pred = model.predict(x, verbose=0)
|
||||
y_pred_classes = np.argmax(y_pred, axis=1)
|
||||
|
||||
recall = sklearn.metrics.recall_score(y, y_pred_classes, average='weighted')
|
||||
precision = sklearn.metrics.precision_score(y, y_pred_classes, average='weighted')
|
||||
f1_score = sklearn.metrics.f1_score(y, y_pred_classes, average='weighted')
|
||||
return pd.DataFrame({split_str:[split_id], threshold_str:[threshold_id], timespan_str:[time_span_id],
|
||||
sequence_length_str:[sequence_length],
|
||||
model_type_str:[model_type], recall_str:[recall],
|
||||
precision_str:[precision], f1_string:[f1_score]})
|
||||
|
||||
def visualise_results_v1():
|
||||
results = pd.DataFrame(json.load(open(result_filename_v1)))
|
||||
# Month split ist immer schlechter
|
||||
results = results[results[split_str] == data_split_str]
|
||||
with_threshold = results[results[threshold_str] == with_threshold_str]
|
||||
without_threshold = results[results[threshold_str] == without_threshold_str]
|
||||
fig, axes = plt.subplots(2, 3)
|
||||
ax_col_id = 0
|
||||
ax_row_id = -1
|
||||
for timespan in [hour_timespan_str,min_timespan_str]:
|
||||
ax_row_id +=1
|
||||
for model in [model_type_lstm, model_type_bilstm, model_type_gru]:
|
||||
with_sub = with_threshold[(with_threshold[timespan_str] == timespan) & (with_threshold[model_type_str] == model)]
|
||||
without_sub = without_threshold[(without_threshold[timespan_str] == timespan) & (without_threshold[model_type_str] == model)]
|
||||
ax = axes[ax_row_id, ax_col_id]
|
||||
ax.set_title(model+' '+timespan)
|
||||
ax.plot(with_sub[sequence_length_str], with_sub[f1_string], label=with_threshold_str)
|
||||
ax.plot(without_sub[sequence_length_str], without_sub[f1_string], label=without_threshold_str)
|
||||
ax.legend()
|
||||
ax_col_id +=1
|
||||
ax_col_id %= 3
|
||||
fig.tight_layout()
|
||||
fig.savefig(figure_path+'v1_results.svg')
|
||||
# Fazit: keine eindeutig besseren Versionen erkennbar
|
||||
|
||||
|
||||
def visualise_results_v2():
|
||||
results = pd.DataFrame(json.load(open(result_filename_v2)))
|
||||
with_threshold = results[results[threshold_str] == with_threshold_str]
|
||||
without_threshold = results[results[threshold_str] == without_threshold_str]
|
||||
fig, axes = plt.subplots(2, 3)
|
||||
ax_col_id = 0
|
||||
ax_row_id = -1
|
||||
for timespan in [hour_timespan_str,min_timespan_str]:
|
||||
ax_row_id +=1
|
||||
for model in [model_type_lstm, model_type_bilstm, model_type_gru]:
|
||||
with_sub = with_threshold[(with_threshold[timespan_str] == timespan) & (with_threshold[model_type_str] == model)]
|
||||
without_sub = without_threshold[(without_threshold[timespan_str] == timespan) & (without_threshold[model_type_str] == model)]
|
||||
with_sub = with_sub.sort_values(sequence_length_str)
|
||||
without_sub = without_sub.sort_values(sequence_length_str)
|
||||
ax = axes[ax_row_id, ax_col_id]
|
||||
ax.set_title(model+' '+timespan)
|
||||
ax.plot(with_sub[sequence_length_str], with_sub[f1_string], label=with_threshold_str)
|
||||
ax.plot(without_sub[sequence_length_str], without_sub[f1_string], label=without_threshold_str)
|
||||
ax.legend()
|
||||
ax_col_id +=1
|
||||
ax_col_id %= 3
|
||||
fig.tight_layout()
|
||||
fig.savefig(figure_path+'v2_results.svg')
|
||||
# Fazit: keine eindeutig besseren Versionen erkennbar
|
||||
|
||||
|
||||
def test(model_type):
|
||||
sequence_length = 20
|
||||
data_filename = os.listdir(dataset_path)[0]
|
||||
timespan_id = hour_timespan_str
|
||||
threshold_id = with_threshold_str
|
||||
|
||||
file_path = os.path.join(dataset_path, data_filename)
|
||||
df = load_dataset(file_path)
|
||||
df = remove_covid_data(df)
|
||||
results = pd.DataFrame()
|
||||
|
||||
for percentage in [33,66,100]:
|
||||
print('Percentage:', percentage)
|
||||
tr,val,te = split_data_by_userdata_percentage(df, percentages=(80,10,10),sample=percentage)
|
||||
tr = reduce_columns(tr, data_filename)
|
||||
val = reduce_columns(val, data_filename)
|
||||
te = reduce_columns(te, data_filename)
|
||||
|
||||
user_data_train = prepare_user_data(tr)
|
||||
user_data_val = prepare_user_data(val)
|
||||
|
||||
best_model = train_models_v2(user_data_train, user_data_val,
|
||||
sequence_length=sequence_length,
|
||||
model_type=model_type)
|
||||
|
||||
results = pd.concat([results,
|
||||
evaluate_model_on_test_data(model=best_model,
|
||||
test_df=te,
|
||||
sequence_length=sequence_length,
|
||||
time_span_id=timespan_id,
|
||||
threshold_id=threshold_id,
|
||||
model_type=model_type,
|
||||
split_id=data_split_str)],
|
||||
ignore_index=True)
|
||||
print(results)
|
||||
|
||||
def manual_tuning(model_type):
|
||||
# load dataset
|
||||
sequence_length = 20
|
||||
data_filename = 'ALL32USERS15MIN_WITHTHRESHOLD.xlsx'
|
||||
timespan_id = min_timespan_str
|
||||
threshold_id = with_threshold_str
|
||||
|
||||
file_path = os.path.join(dataset_path, data_filename)
|
||||
df = load_dataset(file_path)
|
||||
df = remove_covid_data(df)
|
||||
|
||||
tr, val, te = split_data_by_userdata_percentage(df, percentages=(80, 10, 10), sample=100)
|
||||
tr = reduce_columns(tr, data_filename)
|
||||
val = reduce_columns(val, data_filename)
|
||||
te = reduce_columns(te, data_filename)
|
||||
|
||||
user_data_train = prepare_user_data(tr)
|
||||
user_data_val = prepare_user_data(val)
|
||||
|
||||
# fit and evaluate model
|
||||
# config
|
||||
repeats = 3
|
||||
n_batch = 1024
|
||||
n_epochs = 500
|
||||
n_neurons = 16
|
||||
l_rate = 1e-4
|
||||
reg = L1L2(l1=0.0, l2=0.0)
|
||||
|
||||
history_list = list()
|
||||
# run diagnostic tests
|
||||
for i in range(repeats):
|
||||
history = train_one_model(user_data_train, user_data_val, n_batch, n_epochs,
|
||||
n_neurons, l_rate, reg,
|
||||
sequence_length=sequence_length,
|
||||
model_type=model_type)
|
||||
history_list.append(history)
|
||||
for metric in ['p', 'r', 'f1']:
|
||||
for history in history_list:
|
||||
plt.plot(history['train_'+metric], color='blue')
|
||||
plt.plot(history['test_'+metric], color='orange')
|
||||
plt.savefig(figure_path+metric+'_e'+str(n_epochs)+'_n'+str(n_neurons)+'_b'+
|
||||
str(n_batch)+'_l'+str(l_rate)+'_diagnostic.png')
|
||||
plt.clf()
|
||||
print('Done')
|
||||
|
||||
|
||||
def upsampling(df):
|
||||
max_user_data = df[user_str].value_counts().max()
|
||||
for user in df[user_str].unique():
|
||||
user_data = df[df[user_str]==user]
|
||||
user_count = user_data.shape[0]
|
||||
times = max_user_data / user_count
|
||||
before_comma = math.floor(times)
|
||||
after_comma = times % 1
|
||||
after_comma_data = user_data.sample(frac=after_comma)
|
||||
for i in range(1, before_comma):
|
||||
df = pd.concat([df, user_data], ignore_index=True)
|
||||
df = pd.concat([df, after_comma_data], ignore_index=True)
|
||||
return df
|
||||
|
||||
|
||||
def manual_tuning_v3(model_type):
|
||||
# TODO: hrs/min
|
||||
sequence_length = 1
|
||||
|
||||
tr, val, te = get_prepared_data_v3(dataset_hrs_path)
|
||||
|
||||
# fit and evaluate model
|
||||
# config
|
||||
repeats = 3
|
||||
n_batch = 1024
|
||||
n_epochs = 10
|
||||
n_neurons = 256
|
||||
n_neurons2 = 512
|
||||
n_neurons3 = 512
|
||||
n_neurons4 = 128
|
||||
l_rate = 1e-2
|
||||
d1 = 256
|
||||
reg1 = L1L2(l1=0.0, l2=0.001)
|
||||
r1 = '0001'
|
||||
reg2 = L1L2(l1=0.0, l2=0.1)
|
||||
r2 = '01'
|
||||
|
||||
history_list = list()
|
||||
# run diagnostic tests
|
||||
for i in range(repeats):
|
||||
history = train_one_model(tr, val, n_batch, n_epochs,
|
||||
n_neurons,n_neurons2, n_neurons3, n_neurons4, l_rate, d1, r1, reg1, r2, reg2,
|
||||
sequence_length=sequence_length,
|
||||
model_type=model_type)
|
||||
history_list.append(history)
|
||||
for metric in ['acc', 'p', 'r', 'f1']:
|
||||
for history in history_list:
|
||||
plt.plot(history['train_'+metric], color='blue')
|
||||
plt.plot(history['test_'+metric], color='orange')
|
||||
plt.savefig(figure_path+'v3/'+metric+get_save_id(n_epochs, n_neurons, n_neurons2, n_neurons3,n_neurons4, n_batch, l_rate, d1, r1, r2)
|
||||
+'.png')
|
||||
plt.clf()
|
||||
print('Done')
|
||||
|
||||
|
||||
|
||||
def calculate_baselines():
|
||||
file_combinations = [(hour_timespan_str, with_threshold_str,'ALL32USERS1HR_WITHTHRESHOLD.xlsx'),
|
||||
(min_timespan_str, with_threshold_str, 'ALL32USERS15MIN_WITHTHRESHOLD.xlsx'),
|
||||
(min_timespan_str, without_threshold_str, 'ALLUSERS32_15MIN_WITHOUTTHREHOLD.xlsx'),
|
||||
(hour_timespan_str, without_threshold_str, 'ALLUSERS_32_1HR_WITHOUT_THRESHOLD.xlsx'),
|
||||
]
|
||||
baseline_res = pd.DataFrame()
|
||||
for timespan_id, threshold_id, filename in file_combinations:
|
||||
file_path = os.path.join(dataset_path, filename)
|
||||
df = load_dataset(file_path)
|
||||
df = remove_covid_data(df)
|
||||
|
||||
_, _, te = split_data_by_userdata_percentage(df, percentages=(80, 10, 10), sample=20)
|
||||
te = reduce_columns(te, filename)
|
||||
user_data_te = prepare_user_data(te)
|
||||
for sequence_length in range(5,30, 5):
|
||||
x, y = prepare_data_for_model(user_data=user_data_te, sequence_length=sequence_length)
|
||||
|
||||
for strategy in ['most_frequent', 'stratified', 'uniform']:
|
||||
cls = DummyClassifier(strategy=strategy)
|
||||
cls.fit(x,y)
|
||||
y_pred = cls.predict(x)
|
||||
acc, p, r, f1 = eval_metrics(y_true=y, y_pred=y_pred)
|
||||
baseline_res = pd.concat([baseline_res,
|
||||
DataFrame({ 'strategy':[strategy], threshold_str:[threshold_id],
|
||||
timespan_str:[timespan_id], sequence_length_str:[sequence_length],
|
||||
accuracy_str:[acc],precision_str:[p],recall_str:[r],
|
||||
f1_string:f1})], ignore_index=True)
|
||||
baseline_res.to_json('baseline_results.json')
|
||||
print('Done')
|
||||
|
||||
def get_prepared_data_v3(filename, sample=100):
|
||||
:param filename: Name of the file for loading
|
||||
:param sample: percentage of the sample in case less data is wanted (e.g., for testing)
|
||||
:param print_unique: To print the number of unique users
|
||||
:return: train, valid, test splits as dicts from user_id to dataframe
|
||||
"""
|
||||
df = pd.read_json(filename)
|
||||
df = remove_covid_data(df)
|
||||
|
||||
# remove users with too little data (optional)
|
||||
value_counts = df[user_str].value_counts()
|
||||
# df = df[df[user_str].isin(value_counts[value_counts>1000].index)]
|
||||
# remove users which are a complete subset of another user (but keep one)
|
||||
users_to_remove = []
|
||||
for user_a in df[user_str].unique():
|
||||
for user_b in df[user_str].unique():
|
||||
if user_a != user_b:
|
||||
data = pd.concat([df[df[user_str]==user_a], df[df[user_str]==user_b]])
|
||||
columns = data.columns.tolist()
|
||||
columns.remove(user_str)
|
||||
|
||||
adjusted_df = pd.DataFrame()
|
||||
# adjust labels
|
||||
new_id = 0
|
||||
for user_id in df[user_str].unique():
|
||||
user_data = df[df[user_str]==user_id]
|
||||
user_data[user_str] = new_id
|
||||
adjusted_df = pd.concat([adjusted_df, user_data], ignore_index=True)
|
||||
new_id += 1
|
||||
no_dup = data.drop_duplicates(columns, keep=False)
|
||||
if len(no_dup[no_dup[user_str]==user_a]) == 0:
|
||||
if print_unique:
|
||||
print(user_a, 'is subset of',user_b)
|
||||
if user_b not in users_to_remove:
|
||||
users_to_remove.append(user_a)
|
||||
df = df[~df[user_str].isin(users_to_remove)]
|
||||
|
||||
# bin steps per hour TODO: adjust for minutes
|
||||
for hour in ['Hour_'+str(i) for i in range(24)]:
|
||||
hour_data = adjusted_df[hour]
|
||||
hour_data = df[hour]
|
||||
# smaller 1000 - round to 10
|
||||
a = ((hour_data[hour_data<1000]/10).round()*10)
|
||||
# between 1000 and 10000 - round to next 100
|
||||
@@ -507,66 +129,306 @@ def get_prepared_data_v3(filename, sample=100):
|
||||
c = hour_data[hour_data > 10000]
|
||||
c = pd.Series(data={ind:10000 for ind in c.index}, index=c.index)
|
||||
new = pd.concat([a, b, c]).sort_index().astype(int)
|
||||
adjusted_df[hour] = new
|
||||
df[hour] = new
|
||||
|
||||
tr, val, te = split_data_by_userdata_percentage(adjusted_df, percentages=(70, 15, 15), sample=sample)
|
||||
tr = reduce_columns_v3(tr)
|
||||
val = reduce_columns_v3(val)
|
||||
te = reduce_columns_v3(te)
|
||||
# remove users with too little data
|
||||
min_datapoints = 500 # 500 leads to at least 75 datapoints in the valid set
|
||||
users_to_remove = set()
|
||||
cols = df.columns.tolist()
|
||||
cols.remove(user_str)
|
||||
reduced = df.drop_duplicates(subset=cols, keep=False)
|
||||
for user_id in df[user_str].unique():
|
||||
subset = df[df[user_str] == user_id]
|
||||
reduced_subset = reduced[reduced[user_str] == user_id]
|
||||
if print_unique:
|
||||
print(user_id, len(subset), len(reduced_subset))
|
||||
if len(reduced_subset) < min_datapoints:
|
||||
users_to_remove.add(user_id)
|
||||
if print_unique:
|
||||
print('removing', user_id)
|
||||
df = df[~df[user_str].isin(users_to_remove)]
|
||||
|
||||
tr, val, te = split_data_by_userdata_percentage(df, percentages=(70, 15, 15), sample=sample)
|
||||
tr = reduce_columns(tr)
|
||||
val = reduce_columns(val)
|
||||
te = reduce_columns(te)
|
||||
|
||||
if print_unique:
|
||||
print('Train: Users', len(tr[user_str].unique()), 'mean num datapoins:', tr[user_str].value_counts().mean())
|
||||
print('Valid: Users', len(val[user_str].unique()), 'mean num datapoins:', val[user_str].value_counts().mean())
|
||||
print('Test: Users', len(te[user_str].unique()), 'mean num datapoins:', te[user_str].value_counts().mean())
|
||||
|
||||
tr, val, te = add_features(tr), add_features(val), add_features(te)
|
||||
|
||||
scaler = MinMaxScaler()
|
||||
scaler.fit(tr.drop(columns=[user_str]))
|
||||
|
||||
return scale_dataset(scaler, tr), scale_dataset(scaler, val), scale_dataset(scaler, te)
|
||||
|
||||
|
||||
def scale_dataset(scaler, df):
|
||||
"""
|
||||
Data scaling function
|
||||
:param scaler: The scaler object
|
||||
:param df: data to scale
|
||||
:return: the scaled data
|
||||
"""
|
||||
y = df[user_str]
|
||||
x_scaled = scaler.transform(df.drop(columns=[user_str]))
|
||||
x_scaled = pd.DataFrame(x_scaled)
|
||||
x_scaled.columns = df.drop(columns=[user_str]).columns
|
||||
|
||||
df_scaled = pd.concat([pd.DataFrame(x_scaled), pd.DataFrame(y)], axis=1)
|
||||
df_scaled.columns = df.columns
|
||||
return prepare_user_data(df_scaled)
|
||||
df_scaled = pd.concat([x_scaled, pd.DataFrame(y.reset_index()[user_str])], axis=1)
|
||||
return convert_to_user_dict(df_scaled)
|
||||
|
||||
def convert_to_user_dict(df):
|
||||
"""
|
||||
Converts the dataframe to a dict of dataframes with the key the user id
|
||||
|
||||
def calculate_baselines_v3():
|
||||
:param df: Complete dataframe
|
||||
:return: the dict of dataframes
|
||||
"""
|
||||
users = df[user_str].unique()
|
||||
return {user: df[df[user_str] == user] for user in users}
|
||||
|
||||
def calculate_baselines():
|
||||
"""
|
||||
Calculates very simple baselines for the scenario and saves them
|
||||
:return the calculated baselines
|
||||
"""
|
||||
file_combinations = [(hour_timespan_str, dataset_hrs_path),
|
||||
# (min_timespan_str, dataset_min_path), # TODO: dataset bining not ready for minutes
|
||||
# (min_timespan_str, dataset_min_path), # TODO: dataset binning not ready for minutes yet, rerun this method when that works
|
||||
]
|
||||
baseline_res = pd.DataFrame()
|
||||
for timespan_id, filename in file_combinations:
|
||||
_, _, te = get_prepared_data_v3(filename)
|
||||
for sequence_length in range(1,30,5):
|
||||
x, y = prepare_data_for_model(user_data=te, sequence_length=sequence_length)
|
||||
str_result_filename = 'baseline_results.json'
|
||||
if os.path.exists(str_result_filename):
|
||||
baseline_res = pd.read_json(str_result_filename)
|
||||
else:
|
||||
baseline_res = pd.DataFrame()
|
||||
|
||||
for strategy in ['most_frequent', 'stratified', 'uniform']:
|
||||
cls = DummyClassifier(strategy=strategy)
|
||||
cls.fit(x,y)
|
||||
y_pred = cls.predict(x)
|
||||
acc, p, r, f1 = eval_metrics(y_true=y, y_pred=y_pred)
|
||||
baseline_res = pd.concat([baseline_res,
|
||||
DataFrame({ 'strategy':[strategy],
|
||||
timespan_str:[timespan_id], sequence_length_str:[sequence_length],
|
||||
accuracy_str:[acc],precision_str:[p],recall_str:[r],
|
||||
f1_string:f1})], ignore_index=True)
|
||||
baseline_res.to_json('baseline_results_v3.json')
|
||||
for timespan_id, filename in file_combinations:
|
||||
_, _, te = filter_and_preprocess_data(filename)
|
||||
for sequence_length in range(1,30,5):
|
||||
x, y = prepare_data_for_neural_model(user_data=te, sequence_length=sequence_length)
|
||||
|
||||
for strategy in ['most_frequent', 'stratified', 'uniform']:
|
||||
cls = DummyClassifier(strategy=strategy)
|
||||
cls.fit(x,y)
|
||||
y_pred = cls.predict(x)
|
||||
acc, p, r, f1 = eval_metrics(y_true=y, y_pred=y_pred)
|
||||
baseline_res = pd.concat([baseline_res,
|
||||
DataFrame({ 'strategy':[strategy],
|
||||
timespan_str:[timespan_id], sequence_length_str:[sequence_length],
|
||||
accuracy_str:[acc],precision_str:[p],recall_str:[r],
|
||||
f1_string:f1})], ignore_index=True)
|
||||
baseline_res.to_json(str_result_filename)
|
||||
return baseline_res
|
||||
|
||||
def hypertune_basic_algorithms():
|
||||
"""
|
||||
Function can be used to hypertune basic sklearn algorithms. Takes very long.
|
||||
"""
|
||||
# TODO: mnake it run for minutes, iterate over sequence lengths
|
||||
sequence_length = 7
|
||||
|
||||
tr, val, te = filter_and_preprocess_data(dataset_hrs_path)
|
||||
|
||||
x_tr, y_tr = prepare_data_for_basic_algorithm(user_data=tr, sequence_length=sequence_length)
|
||||
x_val, y_val = prepare_data_for_basic_algorithm(user_data=val, sequence_length=sequence_length)
|
||||
|
||||
random_state = 17
|
||||
results = pd.DataFrame()
|
||||
for tag, clf, grid in [
|
||||
('GradientBoosting', GradientBoostingClassifier(random_state=random_state),
|
||||
{'loss': ['log_loss', 'exponential'],
|
||||
'learning_rate': [0.1, 0.5, 1.0,2.0, 5.0],
|
||||
'n_estimators': [10, 50, 100, 150, 200],
|
||||
'subsample': [0.1, 0.5, 1.0],
|
||||
'criterion': ['friedman_mse', 'squared_error'],
|
||||
'min_samples_split': [2, 10, 100],
|
||||
'min_samples_leaf': [1, 5, 10],
|
||||
'min_weight_fraction_leaf': [0.0, 0.1, 0.5],
|
||||
'max_depth': [None, 2, 10, 100],
|
||||
'min_impurity_decrease': [0.0, 0.1, 0.5],
|
||||
'max_features': ['sqrt', 'log2', None, 10, 20],
|
||||
'max_leaf_nodes': [None, 1, 5, 10],
|
||||
}),
|
||||
('Bernoulli', BernoulliNB(), {'fit_prior': [True, False],
|
||||
'binarize': [0.0, 0.1, 0.25, 0.5, 0.75],
|
||||
'force_alpha': [True, False],
|
||||
'alpha':[0.0, 0.25, 0.5, 0.75, 1.0]}),
|
||||
('extra trees', ExtraTreesClassifier(random_state=random_state, n_jobs=1),
|
||||
{'n_estimators': [10, 50, 100, 150, 200],
|
||||
'criterion': ['gini', 'entropy', 'log_loss'],
|
||||
'max_depth': [None, 2, 10, 100],
|
||||
'min_samples_split': [2, 10, 100],
|
||||
'min_samples_leaf': [1, 5, 10],
|
||||
'min_weight_fraction_leaf': [0.0, 0.1, 0.5],
|
||||
'max_features': ['sqrt', 'log2', None, 10, 20],
|
||||
'max_leaf_nodes': [None, 1, 5, 10],
|
||||
'min_impurity_decrease': [0.0, 0.1, 0.5],
|
||||
'bootstrap': [True, False],
|
||||
'class_weight': [None, 'balanced', 'balanced_subsample'],
|
||||
'max_samples': [None, 0.1, 0.2, 0.3]}
|
||||
),
|
||||
('random forest', RandomForestClassifier(random_state=random_state, n_jobs=1),
|
||||
{'n_estimators':[10, 50, 100, 150, 200],
|
||||
'criterion':['gini', 'entropy', 'log_loss'],
|
||||
'max_depth':[None, 2, 10,100],
|
||||
'min_samples_split': [2,10,100],
|
||||
'min_samples_leaf':[1,5,10],
|
||||
'min_weight_fraction_leaf':[0.0,0.1, 0.5],
|
||||
'max_features':['sqrt', 'log2', None, 10, 20],
|
||||
'max_leaf_nodes':[None, 1, 5, 10],
|
||||
'min_impurity_decrease':[0.0, 0.1, 0.5],
|
||||
'bootstrap':[True, False],
|
||||
'class_weight':[None, 'balanced', 'balanced_subsample'],
|
||||
'max_samples':[None,0.1, 0.2, 0.3]})
|
||||
]:
|
||||
grid_search = GridSearchCV(
|
||||
estimator=clf, param_grid=grid, scoring='f1_weighted', cv=5, n_jobs=1)
|
||||
grid_search.fit(x_tr, y_tr)
|
||||
|
||||
best_model = grid_search.best_estimator_
|
||||
y_pred = best_model.predict(x_val)
|
||||
acc, p, r, f1 = eval_metrics(y_true=y_val, y_pred=y_pred)
|
||||
results = pd.concat([results, DataFrame({ 'params': str(grid_search.best_params_),
|
||||
'tag':tag,accuracy_str:[acc],precision_str:[p],recall_str:[r],f1_string:f1})], ignore_index=True)
|
||||
results.to_json('basic_ht_results.json')
|
||||
print('Done')
|
||||
|
||||
def test_basic_algorithms():
|
||||
"""
|
||||
Method for testing basic algorithms from the sklearn library. Tested more, but those 4 were the best
|
||||
:return: The calculated values
|
||||
"""
|
||||
# TODO: also check for minutes
|
||||
# TODO: iterate over sequence lengths
|
||||
sequence_length = 21
|
||||
|
||||
tr, val, te = filter_and_preprocess_data(dataset_hrs_path)
|
||||
|
||||
x_tr, y_tr = prepare_data_for_basic_algorithm(user_data=tr, sequence_length=sequence_length)
|
||||
x_val, y_val = prepare_data_for_basic_algorithm(user_data=val, sequence_length=sequence_length)
|
||||
|
||||
random_state = 17
|
||||
results = pd.DataFrame()
|
||||
for tag, clf in [
|
||||
('Bernoulli', BernoulliNB()),
|
||||
('GradientBoosting', GradientBoostingClassifier(random_state=random_state)),
|
||||
('extra trees', ExtraTreesClassifier(random_state=random_state)),
|
||||
('random forest', RandomForestClassifier(random_state=random_state))
|
||||
]:
|
||||
clf.fit(x_tr, y_tr)
|
||||
y_pred = clf.predict(x_val)
|
||||
acc, p, r, f1 = eval_metrics(y_true=y_val, y_pred=y_pred)
|
||||
results = pd.concat([results, DataFrame({ 'tag':tag,accuracy_str:[acc],precision_str:[p],recall_str:[r],f1_string:f1})], ignore_index=True)
|
||||
return results
|
||||
|
||||
def add_features(df):
|
||||
"""
|
||||
Functiion for adding additional features to the dataframe
|
||||
|
||||
:param df: dataframe
|
||||
:return: dataframe with features added
|
||||
"""
|
||||
# indicator weekend
|
||||
df['weekend'] = df[day_of_week_str + '_Saturday']+df[day_of_week_str + '_Sunday']
|
||||
# sum of steps per day
|
||||
df['day_total'] = sum([df['Hour_'+str(i)] for i in range(23)])
|
||||
# sum of steps morning, afternoon, evening, night
|
||||
df['morning_total'] = sum([df['Hour_' + str(i)] for i in range(6,13)])
|
||||
df['afternoon_total'] = sum([df['Hour_' + str(i)] for i in range(13,19)])
|
||||
df['evening_total'] = sum([df['Hour_' + str(i)] for i in range(19,23)])
|
||||
df['night_total'] = sum([df['Hour_' + str(i)] for i in [23,0,1,2,3,4,5]])
|
||||
return df
|
||||
|
||||
def test_basic_algorithm_on_sequence_lengths(clf = RandomForestClassifier(random_state=17)):
|
||||
"""
|
||||
Runs a basic algorith from scikit learn on different sequence lengths. Plots an image for the results.
|
||||
:param clf: the sklearn classifier
|
||||
"""
|
||||
tr, val, te = filter_and_preprocess_data(dataset_hrs_path)
|
||||
|
||||
results_train = pd.DataFrame()
|
||||
results_valid = pd.DataFrame()
|
||||
# iterate over sequence lengths
|
||||
for sequence_length in range(1, 60, 5):
|
||||
x_tr, y_tr = prepare_data_for_basic_algorithm(user_data=tr, sequence_length=sequence_length)
|
||||
x_val, y_val = prepare_data_for_basic_algorithm(user_data=val, sequence_length=sequence_length)
|
||||
clf.fit(x_tr, y_tr)
|
||||
acc, p, r, f1 = eval_metrics(y_true=y_val, y_pred=clf.predict(x_val))
|
||||
results_valid = pd.concat([results_valid, DataFrame({sequence_length_str:[sequence_length], accuracy_str:[acc],precision_str:[p],recall_str:[r],f1_string:f1})], ignore_index=True)
|
||||
acc, p, r, f1 = eval_metrics(y_true=y_tr, y_pred=clf.predict(x_tr))
|
||||
results_train = pd.concat([results_train, DataFrame({sequence_length_str:[sequence_length], accuracy_str:[acc],precision_str:[p],recall_str:[r],f1_string:f1})], ignore_index=True)
|
||||
|
||||
fig = plt.figure()
|
||||
|
||||
for frame in [results_train, results_valid]:
|
||||
plt.plot(frame[sequence_length_str], frame[f1_string])
|
||||
|
||||
plt.show()
|
||||
print('')
|
||||
|
||||
def tune_neural_network(model_type):
|
||||
"""
|
||||
Tunes a neural model for different sequence lengths. Plots the results
|
||||
:param model_type: either lstm, gru or bilstm, use set strings
|
||||
"""
|
||||
# TODO: Also do this for minute data
|
||||
tr, val, te = filter_and_preprocess_data(dataset_hrs_path)
|
||||
n_epochs= 20
|
||||
n_neurons = 1024
|
||||
n_batch = 1024
|
||||
results_train = pd.DataFrame()
|
||||
results_valid = pd.DataFrame()
|
||||
# iterate over sequence lengths
|
||||
for sequence_length in range(1, 50, 5):
|
||||
train_data = prepare_data_for_neural_model(user_data=tr, sequence_length=sequence_length)
|
||||
val_data = prepare_data_for_neural_model(user_data=val, sequence_length=sequence_length)
|
||||
|
||||
# fit and evaluate model
|
||||
history_list = list()
|
||||
repeats = 3
|
||||
# run diagnostic tests
|
||||
for i in range(repeats):
|
||||
history = train_one_model(train_data, val_data, n_batch, n_epochs,
|
||||
n_neurons, sequence_length=sequence_length,
|
||||
model_type=model_type)
|
||||
history_list.append(history)
|
||||
results = pd.concat([history.tail(1) for history in history_list]).mean()
|
||||
results_train = pd.concat([results_train,
|
||||
DataFrame({sequence_length_str:[sequence_length],
|
||||
accuracy_str:[results['train_acc']],
|
||||
precision_str:[results['train_p']],
|
||||
recall_str:[results['train_r']],
|
||||
f1_string:[results['train_f1']]})], ignore_index=True)
|
||||
results_valid = pd.concat([results_valid,
|
||||
DataFrame({sequence_length_str:[sequence_length],
|
||||
accuracy_str:[results['test_acc']],
|
||||
precision_str:[results['test_p']],
|
||||
recall_str:[results['test_r']],
|
||||
f1_string:[results['test_f1']]})], ignore_index=True)
|
||||
|
||||
fig = plt.figure()
|
||||
for frame in [results_train, results_valid]:
|
||||
plt.plot(frame[sequence_length_str], frame[f1_string])
|
||||
|
||||
plt.show()
|
||||
print('Done')
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Ordner erstellen, die benötigt werden
|
||||
# create needed directories
|
||||
create_dir('results/')
|
||||
create_dir(figure_path)
|
||||
pd.options.mode.copy_on_write = True
|
||||
|
||||
# main_two_v1()
|
||||
# visualise_results_v1()
|
||||
#test(model_type=model_type_gru)
|
||||
# main_two_v2(model_type=model_type_gru)
|
||||
#visualise_results_v2()
|
||||
#manual_tuning(model_type=model_type_lstm)
|
||||
#calculate_baselines()
|
||||
baselines = calculate_baselines()
|
||||
|
||||
#### Ab hier aktuell (21.01.2026)
|
||||
#calculate_baselines_v3()
|
||||
manual_tuning_v3(model_type=model_type_lstm)
|
||||
# use basic algorithms from scikit learn
|
||||
test_basic_algorithms()
|
||||
hypertune_basic_algorithms()
|
||||
test_basic_algorithm_on_sequence_lengths()
|
||||
|
||||
# tune a neural network
|
||||
tune_neural_network(model_type=model_type_lstm)
|
||||
print('Done')
|
||||
File diff suppressed because one or more lines are too long
+848
@@ -0,0 +1,848 @@
|
||||
import json
|
||||
import os
|
||||
|
||||
import math
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import sklearn
|
||||
from keras.src.regularizers import L1L2
|
||||
from matplotlib import pyplot as plt
|
||||
from pandas import DataFrame
|
||||
from sklearn.calibration import CalibratedClassifierCV
|
||||
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis, LinearDiscriminantAnalysis
|
||||
from sklearn.dummy import DummyClassifier
|
||||
from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, BaggingClassifier, VotingClassifier, \
|
||||
GradientBoostingClassifier, AdaBoostClassifier
|
||||
from sklearn.gaussian_process import GaussianProcessClassifier
|
||||
from sklearn.linear_model import PassiveAggressiveClassifier, RidgeClassifier, RidgeClassifierCV, SGDClassifier, \
|
||||
LogisticRegression, LogisticRegressionCV, Perceptron
|
||||
from sklearn.metrics import confusion_matrix
|
||||
from sklearn.mixture import GaussianMixture
|
||||
from sklearn.model_selection import GridSearchCV
|
||||
from sklearn.naive_bayes import GaussianNB, BernoulliNB, MultinomialNB
|
||||
from sklearn.neighbors import KNeighborsClassifier, NearestCentroid
|
||||
from sklearn.neural_network import MLPClassifier
|
||||
from sklearn.preprocessing import MinMaxScaler
|
||||
from sklearn.semi_supervised import LabelSpreading, LabelPropagation
|
||||
from sklearn.svm import LinearSVC, SVC, OneClassSVM
|
||||
from sklearn.tree import ExtraTreeClassifier, DecisionTreeClassifier
|
||||
|
||||
from pipeline_old import (
|
||||
load_dataset,
|
||||
filter_data,
|
||||
filter_test_data,
|
||||
prepare_user_data,
|
||||
train_models,
|
||||
evaluate_models,
|
||||
prepare_data_for_model, model_type_gru, model_type_lstm, model_type_bilstm, train_models_v2, train_one_model,
|
||||
eval_metrics, get_save_id, prepare_data_for_basic_algorithm, train_one_model_v2,
|
||||
)
|
||||
|
||||
year_str = 'Year'
|
||||
month_str = 'Month'
|
||||
day_str = 'Day'
|
||||
date_str = 'Date'
|
||||
time_str = 'Time'
|
||||
day_of_week_str = 'DayOfWeek'
|
||||
user_str = 'user'
|
||||
split_str = 'split type'
|
||||
data_split_str = 'data percentages'
|
||||
month_split_str = 'month percentages'
|
||||
threshold_str = 'threshold used'
|
||||
with_threshold_str = 'WITH'
|
||||
without_threshold_str = 'WITHOUT'
|
||||
timespan_str = 'time used'
|
||||
hour_timespan_str = '1HR'
|
||||
min_timespan_str = '15MIN'
|
||||
sequence_length_str = 'sequence length'
|
||||
accuracy_str = 'accuracy'
|
||||
precision_str = 'precision'
|
||||
recall_str = 'recall'
|
||||
f1_string = 'f1 score'
|
||||
model_type_str = 'model type'
|
||||
week_column_names = ['DayOfWeek_' + day for day in
|
||||
['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday' ]]
|
||||
figure_path = 'figures/'
|
||||
predicitons_path = 'preds/'
|
||||
|
||||
# === Configurable Parameters ===
|
||||
dataset_path = './Datasets/'
|
||||
dataset_hrs_path = './Datasets/hours.json'
|
||||
dataset_min_path = './Datasets/minutes.json'
|
||||
DATA_PATH = dataset_path +'ALLUSERS32_15MIN_WITHOUTTHREHOLD.xlsx'
|
||||
OUTPUT_EXCEL_PATH = './working/evaluation_results.xlsx'
|
||||
result_filename_v1 = './working/evaluation_results.json'
|
||||
result_filename_v2 = './working/evaluation_results_v2.json'
|
||||
SEQUENCE_LENGTHS = [30, 25, 20, 15, 10, 5] # You can add more: [20, 25, 30]
|
||||
|
||||
TRAINING_SCENARIO = [(2018, list(range(1, 13))), (2019, list(range(1, 10)))]
|
||||
VALIDATION_SCENARIO = [(2019, [10, 11, 12])]
|
||||
TEST_SCENARIO = [(2020, [1, 2])] # Jan–Feb 2020 only
|
||||
|
||||
# === Optional display only ===
|
||||
predefined_training_scenarios = {
|
||||
"Scenario 1": {"years_months": [(2018, list(range(1, 13))), (2019, list(range(1, 10)))]},
|
||||
"Scenario 2": {"years_months": [(2017, list(range(1, 13))), (2018, list(range(1, 13))), (2019, list(range(1, 10)))]}
|
||||
}
|
||||
predefined_validation_scenarios = {
|
||||
"Scenario A": {"years_months": [(2019, [10, 11, 12])]}
|
||||
}
|
||||
|
||||
def create_dir(path):
|
||||
"""
|
||||
Creates a directory if it doesn't exist yet.
|
||||
|
||||
:param path: The path to the directory
|
||||
"""
|
||||
if not os.path.exists(path):
|
||||
os.makedirs(path)
|
||||
|
||||
def remove_covid_data(df):
|
||||
df = df[~(df[year_str]>=2020)]
|
||||
return df
|
||||
|
||||
def split_data_by_month_percentage(df, percentages):
|
||||
train_p, valid_p, test_p = percentages
|
||||
ids = df[[year_str, month_str]].drop_duplicates().sort_values([year_str, month_str])
|
||||
tr, va, te = np.split(ids, [int((train_p/100) * len(ids)), int(((train_p + valid_p)/100) * len(ids))])
|
||||
return df.merge(tr, on=[year_str, month_str], how='inner'), df.merge(va, on=[year_str, month_str], how='inner'), df.merge(te, on=[year_str, month_str], how='inner')
|
||||
|
||||
def split_data_by_userdata_percentage(df, percentages, sample=100):
|
||||
train_p, valid_p, test_p = percentages
|
||||
tr, va, te = pd.DataFrame(), pd.DataFrame(), pd.DataFrame()
|
||||
for user_id in df[user_str].unique():
|
||||
# !! following sample creates gaps in data if sample smaller 100
|
||||
user_data = df[df[user_str]==user_id].sample(frac=sample/ 100).sort_values([date_str]) # have to sort for time shift
|
||||
u_tr, u_va, u_te = np.split(user_data, [int((train_p/100)*len(user_data)), int(((train_p+valid_p)/100)*len(user_data))])
|
||||
tr = pd.concat([tr, u_tr], ignore_index=True)
|
||||
va = pd.concat([va, u_va], ignore_index=True)
|
||||
te = pd.concat([te, u_te], ignore_index=True)
|
||||
return tr, va, te
|
||||
|
||||
|
||||
def main():
|
||||
# print("=== Training Scenario Setup ===")
|
||||
# display_warning_about_2020_data()
|
||||
# display_warnings_for_scenarios("training", predefined_training_scenarios, predefined_validation_scenarios)
|
||||
|
||||
# print("\n=== Validation Scenario Setup ===")
|
||||
# display_warning_about_2020_data()
|
||||
# display_warnings_for_scenarios("validation", predefined_training_scenarios, predefined_validation_scenarios)
|
||||
|
||||
# === Load and preprocess ===
|
||||
df = load_dataset(DATA_PATH)
|
||||
|
||||
ALLUSERS32_15MIN_WITHOUTTHREHOLD = False
|
||||
if('ALLUSERS32_15MIN_WITHOUTTHREHOLD.xlsx' in DATA_PATH):
|
||||
ALLUSERS32_15MIN_WITHOUTTHREHOLD = True
|
||||
|
||||
training_data = filter_data(df, TRAINING_SCENARIO, ALLUSERS32_15MIN_WITHOUTTHREHOLD)
|
||||
validation_data = filter_data(df, VALIDATION_SCENARIO, ALLUSERS32_15MIN_WITHOUTTHREHOLD)
|
||||
|
||||
user_data_train = prepare_user_data(training_data)
|
||||
user_data_val = prepare_user_data(validation_data)
|
||||
|
||||
# === Train models ===
|
||||
best_models = train_models(user_data_train, user_data_val, sequence_lengths=SEQUENCE_LENGTHS)
|
||||
|
||||
# === Load and evaluate test ===
|
||||
test_df = filter_test_data(df, TEST_SCENARIO)
|
||||
evaluate_models(best_models, test_df, SEQUENCE_LENGTHS, OUTPUT_EXCEL_PATH, ALLUSERS32_15MIN_WITHOUTTHREHOLD)
|
||||
|
||||
print(f"\n✅ All evaluations completed. Results saved to: {OUTPUT_EXCEL_PATH}")
|
||||
|
||||
|
||||
def reduce_columns(df, filename):
|
||||
if min_timespan_str in filename:
|
||||
return df.drop(columns=['Month', 'Year', 'date', 'DayOfWeek'] + week_column_names, errors='ignore')
|
||||
else:
|
||||
return df.drop(columns=['Month', 'Year', 'date', 'DayOfWeek'], errors='ignore')
|
||||
|
||||
|
||||
def reduce_columns_v3(df):
|
||||
return df.drop(columns=[month_str, year_str, date_str])
|
||||
|
||||
def load_previous_results(filename):
|
||||
results = pd.DataFrame()
|
||||
if os.path.exists(filename):
|
||||
results = pd.DataFrame(json.load(open(filename)))
|
||||
return results
|
||||
|
||||
def main_two_v2(model_type):
|
||||
seq_length = range(10,31, 5)
|
||||
for sequence_length in seq_length:
|
||||
for data_filename in os.listdir(dataset_path):
|
||||
timespan_id = hour_timespan_str
|
||||
threshold_id = with_threshold_str
|
||||
if min_timespan_str in data_filename:
|
||||
timespan_id = min_timespan_str
|
||||
if without_threshold_str in data_filename:
|
||||
threshold_id = without_threshold_str
|
||||
|
||||
results = load_previous_results(result_filename_v2)
|
||||
if len(results) > 0:
|
||||
if len(results[(results[timespan_str]==timespan_id) &
|
||||
(results[threshold_str]==threshold_id) &
|
||||
(results[sequence_length_str]==sequence_length) &
|
||||
(results[model_type_str]==model_type)]) > 0:
|
||||
continue
|
||||
|
||||
file_path = os.path.join(dataset_path, data_filename)
|
||||
df = load_dataset(file_path)
|
||||
df = remove_covid_data(df)
|
||||
|
||||
tr,val,te = split_data_by_userdata_percentage(df, percentages=(80,10,10))
|
||||
tr = reduce_columns(tr, data_filename)
|
||||
val = reduce_columns(val, data_filename)
|
||||
te = reduce_columns(te, data_filename)
|
||||
|
||||
user_data_train = prepare_user_data(tr)
|
||||
user_data_val = prepare_user_data(val)
|
||||
|
||||
best_model = train_models_v2(user_data_train, user_data_val,
|
||||
sequence_length=sequence_length,
|
||||
model_type=model_type)
|
||||
|
||||
results = load_previous_results(result_filename_v2)
|
||||
results = pd.concat([results,
|
||||
evaluate_model_on_test_data(model=best_model,
|
||||
test_df=te,
|
||||
sequence_length=sequence_length,
|
||||
time_span_id=timespan_id,
|
||||
threshold_id=threshold_id,
|
||||
model_type=model_type,
|
||||
split_id=data_split_str)],
|
||||
ignore_index=True)
|
||||
results.to_json(result_filename_v2)
|
||||
|
||||
def main_two_v1():
|
||||
seq_length = [30, 25, 20, 15, 10, 5] # You can add more: [20, 25, 30]
|
||||
results = pd.DataFrame()
|
||||
if os.path.exists(result_filename_v1):
|
||||
results = pd.DataFrame(json.load(open(result_filename_v1)))
|
||||
for sequence_length in seq_length:
|
||||
for data_filename in os.listdir(dataset_path):
|
||||
for split_id, split_method in [(data_split_str, split_data_by_userdata_percentage),(month_split_str, split_data_by_month_percentage)]:
|
||||
for model_type in [model_type_lstm, model_type_bilstm, model_type_gru]:
|
||||
timespan_id = hour_timespan_str
|
||||
threshold_id = with_threshold_str
|
||||
if min_timespan_str in data_filename:
|
||||
timespan_id = min_timespan_str
|
||||
if without_threshold_str in data_filename:
|
||||
threshold_id = without_threshold_str
|
||||
if len(results) > 0:
|
||||
if len(results[(results[split_str]==split_id) &
|
||||
(results[timespan_str]==timespan_id) &
|
||||
(results[threshold_str]==threshold_id) &
|
||||
(results[sequence_length_str]==sequence_length) &
|
||||
(results[model_type_str]==model_type)]) > 0:
|
||||
continue
|
||||
|
||||
file_path = os.path.join(dataset_path, data_filename)
|
||||
df = load_dataset(file_path)
|
||||
df = remove_covid_data(df)
|
||||
tr,val,te = split_method(df, percentages=(80,10,10))
|
||||
tr = reduce_columns(tr, data_filename)
|
||||
val = reduce_columns(val, data_filename)
|
||||
te = reduce_columns(te, data_filename)
|
||||
|
||||
user_data_train = prepare_user_data(tr)
|
||||
user_data_val = prepare_user_data(val)
|
||||
|
||||
best_models = train_models(user_data_train, user_data_val, sequence_lengths=[sequence_length], model_type=model_type)
|
||||
|
||||
results = pd.concat([results,
|
||||
evaluate_model_on_test_data(model=best_models[sequence_length]['model'],
|
||||
test_df=te, split_id=split_id,
|
||||
sequence_length=sequence_length,
|
||||
time_span_id=timespan_id,
|
||||
threshold_id=threshold_id,
|
||||
model_type=model_type)], ignore_index=True)
|
||||
results.to_json(result_filename_v1)
|
||||
|
||||
# === Evaluation ===
|
||||
def evaluate_model_on_test_data(model, test_df,sequence_length, split_id, threshold_id, time_span_id, model_type):
|
||||
user_data = prepare_user_data(test_df)
|
||||
x, y = prepare_data_for_model(user_data=user_data, sequence_length=sequence_length)
|
||||
|
||||
y_pred = model.predict(x, verbose=0)
|
||||
y_pred_classes = np.argmax(y_pred, axis=1)
|
||||
|
||||
recall = sklearn.metrics.recall_score(y, y_pred_classes, average='weighted')
|
||||
precision = sklearn.metrics.precision_score(y, y_pred_classes, average='weighted')
|
||||
f1_score = sklearn.metrics.f1_score(y, y_pred_classes, average='weighted')
|
||||
return pd.DataFrame({split_str:[split_id], threshold_str:[threshold_id], timespan_str:[time_span_id],
|
||||
sequence_length_str:[sequence_length],
|
||||
model_type_str:[model_type], recall_str:[recall],
|
||||
precision_str:[precision], f1_string:[f1_score]})
|
||||
|
||||
def visualise_results_v1():
|
||||
results = pd.DataFrame(json.load(open(result_filename_v1)))
|
||||
# Month split ist immer schlechter
|
||||
results = results[results[split_str] == data_split_str]
|
||||
with_threshold = results[results[threshold_str] == with_threshold_str]
|
||||
without_threshold = results[results[threshold_str] == without_threshold_str]
|
||||
fig, axes = plt.subplots(2, 3)
|
||||
ax_col_id = 0
|
||||
ax_row_id = -1
|
||||
for timespan in [hour_timespan_str,min_timespan_str]:
|
||||
ax_row_id +=1
|
||||
for model in [model_type_lstm, model_type_bilstm, model_type_gru]:
|
||||
with_sub = with_threshold[(with_threshold[timespan_str] == timespan) & (with_threshold[model_type_str] == model)]
|
||||
without_sub = without_threshold[(without_threshold[timespan_str] == timespan) & (without_threshold[model_type_str] == model)]
|
||||
ax = axes[ax_row_id, ax_col_id]
|
||||
ax.set_title(model+' '+timespan)
|
||||
ax.plot(with_sub[sequence_length_str], with_sub[f1_string], label=with_threshold_str)
|
||||
ax.plot(without_sub[sequence_length_str], without_sub[f1_string], label=without_threshold_str)
|
||||
ax.legend()
|
||||
ax_col_id +=1
|
||||
ax_col_id %= 3
|
||||
fig.tight_layout()
|
||||
fig.savefig(figure_path+'v1_results.svg')
|
||||
# Fazit: keine eindeutig besseren Versionen erkennbar
|
||||
|
||||
|
||||
def visualise_results_v2():
|
||||
results = pd.DataFrame(json.load(open(result_filename_v2)))
|
||||
with_threshold = results[results[threshold_str] == with_threshold_str]
|
||||
without_threshold = results[results[threshold_str] == without_threshold_str]
|
||||
fig, axes = plt.subplots(2, 3)
|
||||
ax_col_id = 0
|
||||
ax_row_id = -1
|
||||
for timespan in [hour_timespan_str,min_timespan_str]:
|
||||
ax_row_id +=1
|
||||
for model in [model_type_lstm, model_type_bilstm, model_type_gru]:
|
||||
with_sub = with_threshold[(with_threshold[timespan_str] == timespan) & (with_threshold[model_type_str] == model)]
|
||||
without_sub = without_threshold[(without_threshold[timespan_str] == timespan) & (without_threshold[model_type_str] == model)]
|
||||
with_sub = with_sub.sort_values(sequence_length_str)
|
||||
without_sub = without_sub.sort_values(sequence_length_str)
|
||||
ax = axes[ax_row_id, ax_col_id]
|
||||
ax.set_title(model+' '+timespan)
|
||||
ax.plot(with_sub[sequence_length_str], with_sub[f1_string], label=with_threshold_str)
|
||||
ax.plot(without_sub[sequence_length_str], without_sub[f1_string], label=without_threshold_str)
|
||||
ax.legend()
|
||||
ax_col_id +=1
|
||||
ax_col_id %= 3
|
||||
fig.tight_layout()
|
||||
fig.savefig(figure_path+'v2_results.svg')
|
||||
# Fazit: keine eindeutig besseren Versionen erkennbar
|
||||
|
||||
|
||||
def test(model_type):
|
||||
sequence_length = 20
|
||||
data_filename = os.listdir(dataset_path)[0]
|
||||
timespan_id = hour_timespan_str
|
||||
threshold_id = with_threshold_str
|
||||
|
||||
file_path = os.path.join(dataset_path, data_filename)
|
||||
df = load_dataset(file_path)
|
||||
df = remove_covid_data(df)
|
||||
results = pd.DataFrame()
|
||||
|
||||
for percentage in [33,66,100]:
|
||||
print('Percentage:', percentage)
|
||||
tr,val,te = split_data_by_userdata_percentage(df, percentages=(80,10,10),sample=percentage)
|
||||
tr = reduce_columns(tr, data_filename)
|
||||
val = reduce_columns(val, data_filename)
|
||||
te = reduce_columns(te, data_filename)
|
||||
|
||||
user_data_train = prepare_user_data(tr)
|
||||
user_data_val = prepare_user_data(val)
|
||||
|
||||
best_model = train_models_v2(user_data_train, user_data_val,
|
||||
sequence_length=sequence_length,
|
||||
model_type=model_type)
|
||||
|
||||
results = pd.concat([results,
|
||||
evaluate_model_on_test_data(model=best_model,
|
||||
test_df=te,
|
||||
sequence_length=sequence_length,
|
||||
time_span_id=timespan_id,
|
||||
threshold_id=threshold_id,
|
||||
model_type=model_type,
|
||||
split_id=data_split_str)],
|
||||
ignore_index=True)
|
||||
print(results)
|
||||
|
||||
def manual_tuning(model_type):
|
||||
# load dataset
|
||||
sequence_length = 20
|
||||
data_filename = 'ALL32USERS15MIN_WITHTHRESHOLD.xlsx'
|
||||
timespan_id = min_timespan_str
|
||||
threshold_id = with_threshold_str
|
||||
|
||||
file_path = os.path.join(dataset_path, data_filename)
|
||||
df = load_dataset(file_path)
|
||||
df = remove_covid_data(df)
|
||||
|
||||
tr, val, te = split_data_by_userdata_percentage(df, percentages=(80, 10, 10), sample=100)
|
||||
tr = reduce_columns(tr, data_filename)
|
||||
val = reduce_columns(val, data_filename)
|
||||
te = reduce_columns(te, data_filename)
|
||||
|
||||
user_data_train = prepare_user_data(tr)
|
||||
user_data_val = prepare_user_data(val)
|
||||
|
||||
# fit and evaluate model
|
||||
# config
|
||||
repeats = 3
|
||||
n_batch = 1024
|
||||
n_epochs = 500
|
||||
n_neurons = 16
|
||||
l_rate = 1e-4
|
||||
reg = L1L2(l1=0.0, l2=0.0)
|
||||
|
||||
history_list = list()
|
||||
# run diagnostic tests
|
||||
for i in range(repeats):
|
||||
history = train_one_model(user_data_train, user_data_val, n_batch, n_epochs,
|
||||
n_neurons, l_rate, reg,
|
||||
sequence_length=sequence_length,
|
||||
model_type=model_type)
|
||||
history_list.append(history)
|
||||
for metric in ['p', 'r', 'f1']:
|
||||
for history in history_list:
|
||||
plt.plot(history['train_'+metric], color='blue')
|
||||
plt.plot(history['test_'+metric], color='orange')
|
||||
plt.savefig(figure_path+metric+'_e'+str(n_epochs)+'_n'+str(n_neurons)+'_b'+
|
||||
str(n_batch)+'_l'+str(l_rate)+'_diagnostic.png')
|
||||
plt.clf()
|
||||
print('Done')
|
||||
|
||||
|
||||
def upsampling(df):
|
||||
max_user_data = df[user_str].value_counts().max()
|
||||
for user in df[user_str].unique():
|
||||
user_data = df[df[user_str]==user]
|
||||
user_count = user_data.shape[0]
|
||||
times = max_user_data / user_count
|
||||
before_comma = math.floor(times)
|
||||
after_comma = times % 1
|
||||
after_comma_data = user_data.sample(frac=after_comma)
|
||||
for i in range(1, before_comma):
|
||||
df = pd.concat([df, user_data], ignore_index=True)
|
||||
df = pd.concat([df, after_comma_data], ignore_index=True)
|
||||
return df
|
||||
|
||||
|
||||
def manual_tuning_v3(model_type):
|
||||
# TODO: hrs/min
|
||||
sequence_length = 1
|
||||
|
||||
tr, val, te = get_prepared_data_v3(dataset_hrs_path)
|
||||
|
||||
# fit and evaluate model
|
||||
# config
|
||||
repeats = 3
|
||||
n_batch = 1024
|
||||
n_epochs = 10
|
||||
n_neurons = 256
|
||||
n_neurons2 = 512
|
||||
n_neurons3 = 512
|
||||
n_neurons4 = 128
|
||||
l_rate = 1e-2
|
||||
d1 = 256
|
||||
reg1 = L1L2(l1=0.0, l2=0.001)
|
||||
r1 = '0001'
|
||||
reg2 = L1L2(l1=0.0, l2=0.1)
|
||||
r2 = '01'
|
||||
|
||||
history_list = list()
|
||||
# run diagnostic tests
|
||||
for i in range(repeats):
|
||||
history = train_one_model(tr, val, n_batch, n_epochs,
|
||||
n_neurons,n_neurons2, n_neurons3, n_neurons4, l_rate, d1, r1, reg1, r2, reg2,
|
||||
sequence_length=sequence_length,
|
||||
model_type=model_type)
|
||||
history_list.append(history)
|
||||
for metric in ['acc', 'p', 'r', 'f1']:
|
||||
for history in history_list:
|
||||
plt.plot(history['train_'+metric], color='blue')
|
||||
plt.plot(history['test_'+metric], color='orange')
|
||||
plt.savefig(figure_path+'v3/'+metric+get_save_id(n_epochs, n_neurons, n_neurons2, n_neurons3,n_neurons4, n_batch, l_rate, d1, r1, r2)
|
||||
+'.png')
|
||||
plt.clf()
|
||||
print('Done')
|
||||
|
||||
|
||||
|
||||
def calculate_baselines():
|
||||
file_combinations = [(hour_timespan_str, with_threshold_str,'ALL32USERS1HR_WITHTHRESHOLD.xlsx'),
|
||||
(min_timespan_str, with_threshold_str, 'ALL32USERS15MIN_WITHTHRESHOLD.xlsx'),
|
||||
(min_timespan_str, without_threshold_str, 'ALLUSERS32_15MIN_WITHOUTTHREHOLD.xlsx'),
|
||||
(hour_timespan_str, without_threshold_str, 'ALLUSERS_32_1HR_WITHOUT_THRESHOLD.xlsx'),
|
||||
]
|
||||
baseline_res = pd.DataFrame()
|
||||
for timespan_id, threshold_id, filename in file_combinations:
|
||||
file_path = os.path.join(dataset_path, filename)
|
||||
df = load_dataset(file_path)
|
||||
df = remove_covid_data(df)
|
||||
|
||||
_, _, te = split_data_by_userdata_percentage(df, percentages=(80, 10, 10), sample=20)
|
||||
te = reduce_columns(te, filename)
|
||||
user_data_te = prepare_user_data(te)
|
||||
for sequence_length in range(5,30, 5):
|
||||
x, y = prepare_data_for_model(user_data=user_data_te, sequence_length=sequence_length)
|
||||
|
||||
for strategy in ['most_frequent', 'stratified', 'uniform']:
|
||||
cls = DummyClassifier(strategy=strategy)
|
||||
cls.fit(x,y)
|
||||
y_pred = cls.predict(x)
|
||||
acc, p, r, f1 = eval_metrics(y_true=y, y_pred=y_pred)
|
||||
baseline_res = pd.concat([baseline_res,
|
||||
DataFrame({ 'strategy':[strategy], threshold_str:[threshold_id],
|
||||
timespan_str:[timespan_id], sequence_length_str:[sequence_length],
|
||||
accuracy_str:[acc],precision_str:[p],recall_str:[r],
|
||||
f1_string:f1})], ignore_index=True)
|
||||
baseline_res.to_json('baseline_results.json')
|
||||
print('Done')
|
||||
|
||||
def get_prepared_data_v3(filename, sample=100, print_unique=False):
|
||||
df = pd.read_json(filename)
|
||||
df = remove_covid_data(df)
|
||||
|
||||
# remove users which are a complete subset of another user (but keep one)
|
||||
users_to_remove = []
|
||||
for user_a in df[user_str].unique():
|
||||
for user_b in df[user_str].unique():
|
||||
if user_a != user_b:
|
||||
data = pd.concat([df[df[user_str]==user_a], df[df[user_str]==user_b]])
|
||||
columns = data.columns.tolist()
|
||||
columns.remove(user_str)
|
||||
|
||||
no_dup = data.drop_duplicates(columns, keep=False)
|
||||
if len(no_dup[no_dup[user_str]==user_a]) == 0:
|
||||
if print_unique:
|
||||
print(user_a, 'is subset of',user_b)
|
||||
if user_b not in users_to_remove:
|
||||
users_to_remove.append(user_a)
|
||||
df = df[~df[user_str].isin(users_to_remove)]
|
||||
|
||||
# bin steps per hour TODO: adjust for minutes
|
||||
for hour in ['Hour_'+str(i) for i in range(24)]:
|
||||
hour_data = df[hour]
|
||||
# smaller 1000 - round to 10
|
||||
a = ((hour_data[hour_data<1000]/10).round()*10)
|
||||
# between 1000 and 10000 - round to next 100
|
||||
b = ((hour_data[(hour_data>=1000)& (hour_data<10000)]/100).round()*100)
|
||||
# higher or equal 10000 - one class
|
||||
c = hour_data[hour_data > 10000]
|
||||
c = pd.Series(data={ind:10000 for ind in c.index}, index=c.index)
|
||||
new = pd.concat([a, b, c]).sort_index().astype(int)
|
||||
df[hour] = new
|
||||
|
||||
# remove users with too little data (optional)
|
||||
#value_counts = df[user_str].value_counts()
|
||||
#df = df[df[user_str].isin(value_counts[value_counts>200].index)]
|
||||
min_datapoints = 500 # 500 leads to at least 75 datapoints in the valid set
|
||||
users_to_remove = set()
|
||||
cols = df.columns.tolist()
|
||||
cols.remove(user_str)
|
||||
reduced = df.drop_duplicates(subset=cols, keep=False)
|
||||
for user_id in df[user_str].unique():
|
||||
subset = df[df[user_str] == user_id]
|
||||
reduced_subset = reduced[reduced[user_str] == user_id]
|
||||
if print_unique:
|
||||
print(user_id, len(subset), len(reduced_subset))
|
||||
if len(reduced_subset) < min_datapoints:
|
||||
users_to_remove.add(user_id)
|
||||
if print_unique:
|
||||
print('removing', user_id)
|
||||
|
||||
df = df[~df[user_str].isin(users_to_remove)]
|
||||
|
||||
tr, val, te = split_data_by_userdata_percentage(df, percentages=(70, 15, 15), sample=sample)
|
||||
tr = reduce_columns_v3(tr)
|
||||
val = reduce_columns_v3(val)
|
||||
te = reduce_columns_v3(te)
|
||||
|
||||
|
||||
|
||||
print('Train: Users', len(tr[user_str].unique()), 'mean num datapoins:', tr[user_str].value_counts().mean())
|
||||
print('Valid: Users', len(val[user_str].unique()), 'mean num datapoins:', val[user_str].value_counts().mean())
|
||||
print('Test: Users', len(te[user_str].unique()), 'mean num datapoins:', te[user_str].value_counts().mean())
|
||||
|
||||
tr, val, te = add_features(tr), add_features(val), add_features(te)
|
||||
|
||||
scaler = MinMaxScaler()
|
||||
scaler.fit(tr.drop(columns=[user_str]))
|
||||
|
||||
return scale_dataset(scaler, tr), scale_dataset(scaler, val), scale_dataset(scaler, te)
|
||||
|
||||
|
||||
def scale_dataset(scaler, df):
|
||||
y = df[user_str]
|
||||
x_scaled = scaler.transform(df.drop(columns=[user_str]))
|
||||
x_scaled = pd.DataFrame(x_scaled)
|
||||
x_scaled.columns = df.drop(columns=[user_str]).columns
|
||||
|
||||
df_scaled = pd.concat([x_scaled, pd.DataFrame(y.reset_index()[user_str])], axis=1)
|
||||
# df_scaled.columns = df.columns
|
||||
return prepare_user_data(df_scaled)
|
||||
|
||||
|
||||
def calculate_baselines_v3():
|
||||
file_combinations = [(hour_timespan_str, dataset_hrs_path),
|
||||
# (min_timespan_str, dataset_min_path), # TODO: dataset bining not ready for minutes
|
||||
]
|
||||
baseline_res = pd.DataFrame()
|
||||
for timespan_id, filename in file_combinations:
|
||||
_, _, te = get_prepared_data_v3(filename)
|
||||
for sequence_length in range(1,30,5):
|
||||
x, y = prepare_data_for_model(user_data=te, sequence_length=sequence_length)
|
||||
|
||||
for strategy in ['most_frequent', 'stratified', 'uniform']:
|
||||
cls = DummyClassifier(strategy=strategy)
|
||||
cls.fit(x,y)
|
||||
y_pred = cls.predict(x)
|
||||
acc, p, r, f1 = eval_metrics(y_true=y, y_pred=y_pred)
|
||||
baseline_res = pd.concat([baseline_res,
|
||||
DataFrame({ 'strategy':[strategy],
|
||||
timespan_str:[timespan_id], sequence_length_str:[sequence_length],
|
||||
accuracy_str:[acc],precision_str:[p],recall_str:[r],
|
||||
f1_string:f1})], ignore_index=True)
|
||||
baseline_res.to_json('baseline_results_v3.json')
|
||||
print('Done')
|
||||
|
||||
|
||||
def hypertune_basic_algorithms():
|
||||
# TODO: hrs/min
|
||||
# iterate over sequence lengths
|
||||
sequence_length = 7
|
||||
|
||||
tr, val, te = get_prepared_data_v3(dataset_hrs_path)
|
||||
|
||||
x_tr, y_tr = prepare_data_for_basic_algorithm(user_data=tr, sequence_length=sequence_length)
|
||||
x_val, y_val = prepare_data_for_basic_algorithm(user_data=val, sequence_length=sequence_length)
|
||||
|
||||
random_state = 17
|
||||
results = pd.DataFrame()
|
||||
for tag, clf, grid in [
|
||||
('GradientBoosting', GradientBoostingClassifier(random_state=random_state),
|
||||
{'loss': ['log_loss', 'exponential'],
|
||||
'learning_rate': [0.1, 0.5, 1.0,2.0, 5.0],
|
||||
'n_estimators': [10, 50, 100, 150, 200],
|
||||
'subsample': [0.1, 0.5, 1.0],
|
||||
'criterion': ['friedman_mse', 'squared_error'],
|
||||
'min_samples_split': [2, 10, 100],
|
||||
'min_samples_leaf': [1, 5, 10],
|
||||
'min_weight_fraction_leaf': [0.0, 0.1, 0.5],
|
||||
'max_depth': [None, 2, 10, 100],
|
||||
'min_impurity_decrease': [0.0, 0.1, 0.5],
|
||||
'max_features': ['sqrt', 'log2', None, 10, 20],
|
||||
'max_leaf_nodes': [None, 1, 5, 10],
|
||||
}),
|
||||
('Bernoulli', BernoulliNB(), {'fit_prior': [True, False],
|
||||
'binarize': [0.0, 0.1, 0.25, 0.5, 0.75],
|
||||
'force_alpha': [True, False],
|
||||
'alpha':[0.0, 0.25, 0.5, 0.75, 1.0]}),
|
||||
('extra trees', ExtraTreesClassifier(random_state=random_state, n_jobs=1),
|
||||
{'n_estimators': [10, 50, 100, 150, 200],
|
||||
'criterion': ['gini', 'entropy', 'log_loss'],
|
||||
'max_depth': [None, 2, 10, 100],
|
||||
'min_samples_split': [2, 10, 100],
|
||||
'min_samples_leaf': [1, 5, 10],
|
||||
'min_weight_fraction_leaf': [0.0, 0.1, 0.5],
|
||||
'max_features': ['sqrt', 'log2', None, 10, 20],
|
||||
'max_leaf_nodes': [None, 1, 5, 10],
|
||||
'min_impurity_decrease': [0.0, 0.1, 0.5],
|
||||
'bootstrap': [True, False],
|
||||
'class_weight': [None, 'balanced', 'balanced_subsample'],
|
||||
'max_samples': [None, 0.1, 0.2, 0.3]}
|
||||
),
|
||||
('random forest', RandomForestClassifier(random_state=random_state, n_jobs=1),
|
||||
{'n_estimators':[10, 50, 100, 150, 200],
|
||||
'criterion':['gini', 'entropy', 'log_loss'],
|
||||
'max_depth':[None, 2, 10,100],
|
||||
'min_samples_split': [2,10,100],
|
||||
'min_samples_leaf':[1,5,10],
|
||||
'min_weight_fraction_leaf':[0.0,0.1, 0.5],
|
||||
'max_features':['sqrt', 'log2', None, 10, 20],
|
||||
'max_leaf_nodes':[None, 1, 5, 10],
|
||||
'min_impurity_decrease':[0.0, 0.1, 0.5],
|
||||
'bootstrap':[True, False],
|
||||
'class_weight':[None, 'balanced', 'balanced_subsample'],
|
||||
'max_samples':[None,0.1, 0.2, 0.3]})
|
||||
]:
|
||||
grid_search = GridSearchCV(
|
||||
estimator=clf, param_grid=grid, scoring='f1_weighted', cv=5, n_jobs=1)
|
||||
grid_search.fit(x_tr, y_tr)
|
||||
|
||||
best_model = grid_search.best_estimator_
|
||||
y_pred = best_model.predict(x_val)
|
||||
acc, p, r, f1 = eval_metrics(y_true=y_val, y_pred=y_pred)
|
||||
results = pd.concat([results, DataFrame({ 'params': str(grid_search.best_params_),
|
||||
'tag':tag,accuracy_str:[acc],precision_str:[p],recall_str:[r],f1_string:f1})], ignore_index=True)
|
||||
results.to_json('basic_ht_results.json')
|
||||
print('Done')
|
||||
|
||||
def test_basic_algorithms():
|
||||
# TODO: hrs/min
|
||||
# TODO: iterate over sequence lengths
|
||||
sequence_length = 21
|
||||
|
||||
tr, val, te = get_prepared_data_v3(dataset_hrs_path)
|
||||
|
||||
x_tr, y_tr = prepare_data_for_basic_algorithm(user_data=tr, sequence_length=sequence_length)
|
||||
x_val, y_val = prepare_data_for_basic_algorithm(user_data=val, sequence_length=sequence_length)
|
||||
|
||||
random_state = 17
|
||||
results = pd.DataFrame()
|
||||
for tag, clf in [
|
||||
# ('Label Propagation', LabelPropagation()),
|
||||
# ('Label Spreading', LabelSpreading()),
|
||||
# ('VBGMM', GaussianMixture(random_state=random_state)),
|
||||
# ('linear discrimenant analysis', LinearDiscriminantAnalysis()),
|
||||
# ('discriminent analysis', QuadraticDiscriminantAnalysis()),
|
||||
# ('oneclassSVM', OneClassSVM()),
|
||||
# ('mlp', MLPClassifier(random_state=random_state)),
|
||||
# ('Perceptron', Perceptron(random_state=random_state)),
|
||||
# ('SVC', SVC(random_state=random_state)),
|
||||
#('logisticRegression', LogisticRegression(random_state=random_state)),
|
||||
#('logisticRegressionCV', LogisticRegressionCV(random_state=random_state)),
|
||||
#('multinomialNB', MultinomialNB()),
|
||||
#('nearestCentroid', NearestCentroid()),
|
||||
#('linearSVC', LinearSVC(random_state=random_state)),
|
||||
#('ada boost', AdaBoostClassifier(random_state=random_state)),
|
||||
#('GradientBoosting', GradientBoostingClassifier(random_state=random_state)),
|
||||
#('Bernoulli', BernoulliNB()),
|
||||
#('claibrated', CalibratedClassifierCV()),
|
||||
#('naive Bayes', GaussianNB()),
|
||||
#('sgd', SGDClassifier(random_state=random_state)),
|
||||
#('ridgeCV', RidgeClassifierCV()),
|
||||
# ('ridge', RidgeClassifier(random_state=random_state)),
|
||||
# ('passiveAggressive', PassiveAggressiveClassifier(random_state=random_state)),
|
||||
# ('knn', KNeighborsClassifier()),
|
||||
# ('bagging', BaggingClassifier(random_state=random_state)),
|
||||
# ('decision tree', DecisionTreeClassifier(random_state=random_state)),
|
||||
# ('extra tree', ExtraTreeClassifier(random_state=random_state)),
|
||||
# ('extra trees', ExtraTreesClassifier(random_state=random_state)),
|
||||
('random forest', RandomForestClassifier(random_state=random_state))
|
||||
]:
|
||||
clf.fit(x_tr, y_tr)
|
||||
y_pred = clf.predict(x_val)
|
||||
acc, p, r, f1 = eval_metrics(y_true=y_val, y_pred=y_pred)
|
||||
results = pd.concat([results, DataFrame({ 'tag':tag,accuracy_str:[acc],precision_str:[p],recall_str:[r],f1_string:f1})], ignore_index=True)
|
||||
print('Done')
|
||||
|
||||
|
||||
def add_features(df):
|
||||
# indicator weekend
|
||||
df['weekend'] = df[day_of_week_str + '_Saturday']+df[day_of_week_str + '_Sunday']
|
||||
# sum of steps per day
|
||||
df['day_total'] = sum([df['Hour_'+str(i)] for i in range(23)])
|
||||
# sum of steps morning, afternoon, evening, night
|
||||
df['morning_total'] = sum([df['Hour_' + str(i)] for i in range(6,13)])
|
||||
df['afternoon_total'] = sum([df['Hour_' + str(i)] for i in range(13,19)])
|
||||
df['evening_total'] = sum([df['Hour_' + str(i)] for i in range(19,23)])
|
||||
df['night_total'] = sum([df['Hour_' + str(i)] for i in [23,0,1,2,3,4,5]])
|
||||
return df
|
||||
|
||||
|
||||
def feature_engineering():
|
||||
sequence_length = 1
|
||||
|
||||
tr, val, te = get_prepared_data_v3(dataset_hrs_path, print_unique=True)
|
||||
|
||||
x_tr, y_tr = prepare_data_for_basic_algorithm(user_data=tr, sequence_length=sequence_length)
|
||||
x_val, y_val = prepare_data_for_basic_algorithm(user_data=val, sequence_length=sequence_length)
|
||||
|
||||
random_state = 17
|
||||
clf=RandomForestClassifier(random_state=random_state)
|
||||
clf.fit(x_tr, y_tr)
|
||||
y_pred = clf.predict(x_val)
|
||||
acc, p, r, f1 = eval_metrics(y_true=y_val, y_pred=y_pred)
|
||||
cf = confusion_matrix(y_pred=y_pred, y_true=y_val)
|
||||
# TODO: welche funktionieren schlecht? warum?
|
||||
# TODO: auf minutes umändern
|
||||
|
||||
print('Done')
|
||||
|
||||
|
||||
def test_sequence_length_on_approach(clf = RandomForestClassifier(random_state=17)):
|
||||
tr, val, te = get_prepared_data_v3(dataset_hrs_path)
|
||||
|
||||
results_train = pd.DataFrame()
|
||||
results_valid = pd.DataFrame()
|
||||
for sequence_length in range(1, 60, 5):
|
||||
x_tr, y_tr = prepare_data_for_basic_algorithm(user_data=tr, sequence_length=sequence_length)
|
||||
x_val, y_val = prepare_data_for_basic_algorithm(user_data=val, sequence_length=sequence_length)
|
||||
clf.fit(x_tr, y_tr)
|
||||
acc, p, r, f1 = eval_metrics(y_true=y_val, y_pred=clf.predict(x_val))
|
||||
results_valid = pd.concat([results_valid, DataFrame({sequence_length_str:[sequence_length], accuracy_str:[acc],precision_str:[p],recall_str:[r],f1_string:f1})], ignore_index=True)
|
||||
acc, p, r, f1 = eval_metrics(y_true=y_tr, y_pred=clf.predict(x_tr))
|
||||
results_train = pd.concat([results_train, DataFrame({sequence_length_str:[sequence_length], accuracy_str:[acc],precision_str:[p],recall_str:[r],f1_string:f1})], ignore_index=True)
|
||||
|
||||
fig = plt.figure()
|
||||
|
||||
for frame in [results_train, results_valid]:
|
||||
plt.plot(frame[sequence_length_str], frame[f1_string])
|
||||
|
||||
plt.show()
|
||||
print('')
|
||||
|
||||
def manual_tuning_v4(model_type):
|
||||
# TODO: hrs/min
|
||||
tr, val, te = get_prepared_data_v3(dataset_hrs_path)
|
||||
n_epochs= 20
|
||||
n_neurons = 1024
|
||||
results_train = pd.DataFrame()
|
||||
results_valid = pd.DataFrame()
|
||||
for sequence_length in range(1, 50, 5):
|
||||
train_data = prepare_data_for_model(user_data=tr, sequence_length=sequence_length)
|
||||
val_data = prepare_data_for_model(user_data=val, sequence_length=sequence_length)
|
||||
|
||||
# fit and evaluate model
|
||||
history_list = list()
|
||||
repeats = 3
|
||||
# run diagnostic tests
|
||||
for i in range(repeats):
|
||||
history = train_one_model_v2(train_data, val_data, 1024, n_epochs,
|
||||
n_neurons, sequence_length=sequence_length,
|
||||
model_type=model_type)
|
||||
history_list.append(history)
|
||||
results = pd.concat([history.tail(1) for history in history_list]).mean()
|
||||
results_train = pd.concat([results_train,
|
||||
DataFrame({sequence_length_str:[sequence_length],
|
||||
accuracy_str:[results['train_acc']],
|
||||
precision_str:[results['train_p']],
|
||||
recall_str:[results['train_r']],
|
||||
f1_string:[results['train_f1']]})], ignore_index=True)
|
||||
results_valid = pd.concat([results_valid,
|
||||
DataFrame({sequence_length_str:[sequence_length],
|
||||
accuracy_str:[results['test_acc']],
|
||||
precision_str:[results['test_p']],
|
||||
recall_str:[results['test_r']],
|
||||
f1_string:[results['test_f1']]})], ignore_index=True)
|
||||
|
||||
fig = plt.figure()
|
||||
for frame in [results_train, results_valid]:
|
||||
plt.plot(frame[sequence_length_str], frame[f1_string])
|
||||
|
||||
plt.show()
|
||||
print('Done')
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Ordner erstellen, die benötigt werden
|
||||
create_dir('results/')
|
||||
create_dir(figure_path)
|
||||
pd.options.mode.copy_on_write = True
|
||||
|
||||
main_two_v1()
|
||||
visualise_results_v1()
|
||||
#test(model_type=model_type_gru)
|
||||
# main_two_v2(model_type=model_type_gru)
|
||||
#visualise_results_v2()
|
||||
#manual_tuning(model_type=model_type_lstm)
|
||||
#calculate_baselines()
|
||||
|
||||
#### Ab hier aktuell (21.01.2026)
|
||||
#calculate_baselines()
|
||||
# manual_tuning_v3(model_type=model_type_lstm)
|
||||
#test_basic_algorithms()
|
||||
# test_basic_algorithm_on_sequence_lengths()
|
||||
manual_tuning_v4(model_type=model_type_lstm)
|
||||
#feature_engineering()
|
||||
#hypertune_basic_algorithms()
|
||||
print('Done')
|
||||
@@ -0,0 +1,466 @@
|
||||
import random
|
||||
|
||||
import keras_tuner
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import shutil
|
||||
|
||||
from keras import Input
|
||||
from keras.src.losses import SparseCategoricalCrossentropy
|
||||
from keras.src.metrics import F1Score, Precision, Recall, Accuracy, SparseCategoricalAccuracy
|
||||
from pandas import ExcelWriter, DataFrame
|
||||
from tensorflow.keras.models import Sequential
|
||||
from tensorflow.keras.layers import LSTM, Dense, Dropout, Bidirectional,GRU
|
||||
from tensorflow.keras.optimizers import Adam
|
||||
from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping
|
||||
from keras_tuner import RandomSearch
|
||||
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix
|
||||
|
||||
epochs = 5#50
|
||||
model_type_gru = 'GRU'
|
||||
model_type_lstm = 'LSTM'
|
||||
model_type_bilstm = 'BiLSTM'
|
||||
|
||||
|
||||
# === Data functions ===
|
||||
def load_dataset(file_path):
|
||||
return pd.read_excel(file_path)
|
||||
|
||||
def filter_data(df, scenario, ALLUSERS32_15MIN_WITHOUTREHOLD):
|
||||
filtered = pd.DataFrame()
|
||||
for year, months in scenario:
|
||||
filtered = pd.concat([filtered, df[(df['Year'] == year) & (df['Month'].isin(months))]])
|
||||
|
||||
if ALLUSERS32_15MIN_WITHOUTREHOLD:
|
||||
return filtered.drop(columns=['Month', 'Year', 'date', 'DayOfWeek'])
|
||||
else:
|
||||
return filtered.drop(columns=['Month', 'Year', 'date'])
|
||||
|
||||
def filter_test_data(df, scenario):
|
||||
data_parts = []
|
||||
for year, months in scenario:
|
||||
part = df[(df['Year'] == year) & (df['Month'].isin(months))]
|
||||
data_parts.append(part)
|
||||
return pd.concat(data_parts, ignore_index=True)
|
||||
|
||||
def prepare_user_data(df):
|
||||
#df_sorted = df.sort_values(by='user').reset_index(drop=True)
|
||||
users = df['user'].unique()
|
||||
return {user: df[df['user'] == user] for user in users}
|
||||
|
||||
def make_sequences(data, sequence_length):
|
||||
x, y = [], []
|
||||
features = data.drop('user', axis=1).values
|
||||
labels = data['user'].values
|
||||
for i in range(len(features) - sequence_length+1): # with overlap on days
|
||||
# for i in range(0, len(features) - sequence_length + 1, sequence_length): # without overlap on days
|
||||
x.append(features[i:i + sequence_length])
|
||||
y.append(labels[i + sequence_length-1])
|
||||
return x, y
|
||||
|
||||
def prepare_data_for_basic_algorithm(user_data, sequence_length):
|
||||
combined = pd.DataFrame()
|
||||
for user, data in user_data.items():
|
||||
x_new, y_new = make_sequences(data, sequence_length)
|
||||
if len(x_new)>0:
|
||||
var = [[pd.DataFrame(a[s]) for s in range(sequence_length)] for a in x_new]
|
||||
df_var = pd.concat([pd.concat(seq_list).T for seq_list in var])
|
||||
df_var['user'] = user
|
||||
combined = pd.concat([combined, df_var], ignore_index=True)
|
||||
return combined.drop(columns=['user']), combined['user']
|
||||
|
||||
def prepare_data_for_model(user_data, sequence_length, print_counts=False):
|
||||
x, y = [], []
|
||||
combined = pd.DataFrame()
|
||||
for user, data in user_data.items():
|
||||
x_new, y_new = make_sequences(data, sequence_length)
|
||||
x = x + x_new
|
||||
y = y + y_new
|
||||
if print_counts and len(x_new)>0:
|
||||
var = [[pd.DataFrame(a[s])for s in range(sequence_length)] for a in x_new ]
|
||||
df_var = pd.concat([pd.concat(seq_list).T for seq_list in var])
|
||||
df_var['user'] = user
|
||||
combined = pd.concat([combined, df_var], ignore_index=True)
|
||||
if print_counts:
|
||||
combined_ohne = combined.drop('user', axis=1)
|
||||
print('Alle', len(combined))
|
||||
print('Unique mit user', len(combined.drop_duplicates()))
|
||||
print('Unique ohne user', len(combined_ohne.drop_duplicates()))
|
||||
print('Unique')
|
||||
print(combined.drop_duplicates()['user'].value_counts())
|
||||
print('Alle')
|
||||
print(combined['user'].value_counts())
|
||||
random.Random(17).shuffle(x)
|
||||
random.Random(17).shuffle(y)
|
||||
x = np.array(x)
|
||||
y = np.array(y)
|
||||
return x,y
|
||||
|
||||
# === Training & Validation ===
|
||||
def train_models(user_data, user_data_val, sequence_lengths, tuner_dir="./working/tuner", model_type=model_type_lstm):
|
||||
best_models = {}
|
||||
early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)
|
||||
lr_scheduler = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=5, verbose=1)
|
||||
users = list(user_data.keys())
|
||||
|
||||
shutil.rmtree(tuner_dir, ignore_errors=True)
|
||||
|
||||
for sequence_length in sequence_lengths:
|
||||
print(f"\n=== Training for Sequence Length: {sequence_length} ===")
|
||||
X, y = prepare_data_for_model(user_data=user_data, sequence_length=sequence_length)
|
||||
X_val, y_val = prepare_data_for_model(user_data=user_data_val, sequence_length=sequence_length)
|
||||
|
||||
if X.shape[0] == 0 or X_val.shape[0] == 0:
|
||||
print(f"⚠️ Skipped sequence length {sequence_length} due to insufficient data.")
|
||||
continue
|
||||
|
||||
n_features = X.shape[2]
|
||||
|
||||
def build_model(hp):
|
||||
model = Sequential()
|
||||
if model_type==model_type_bilstm:
|
||||
model.add(Bidirectional(LSTM(units=hp.Int('units', 32, 256, step=2),
|
||||
input_shape=(sequence_length, n_features))))
|
||||
if model_type==model_type_lstm:
|
||||
model.add(LSTM(units=hp.Int('units', 32, 256, step=2),
|
||||
input_shape=(sequence_length, n_features)))
|
||||
if model_type==model_type_gru:
|
||||
model.add(GRU(units=hp.Int('units', 32, 256, step=2),
|
||||
input_shape=(sequence_length, n_features)))
|
||||
model.add(Dropout(hp.Float('dropout_rate', 0.1, 0.5, step=0.1)))
|
||||
model.add(Dense(len(users), activation='softmax'))
|
||||
model.compile(
|
||||
optimizer=Adam(learning_rate=hp.Choice('learning_rate', [1e-2, 1e-3, 1e-4])),
|
||||
loss='sparse_categorical_crossentropy',
|
||||
metrics=['accuracy']
|
||||
)
|
||||
return model
|
||||
|
||||
tuner = RandomSearch(
|
||||
build_model,
|
||||
objective='val_loss',
|
||||
max_trials=30,
|
||||
executions_per_trial=2,
|
||||
directory=tuner_dir,
|
||||
project_name=f'lstm_seq_{sequence_length}'
|
||||
)
|
||||
|
||||
tuner.search(X, y, epochs=epochs, validation_data=(X_val, y_val),
|
||||
callbacks=[early_stopping, lr_scheduler], verbose=0)
|
||||
|
||||
best_hps = tuner.get_best_hyperparameters(1)[0]
|
||||
best_model = tuner.hypermodel.build(best_hps)
|
||||
best_model.fit(X, y, epochs=epochs, validation_data=(X_val, y_val),
|
||||
callbacks=[early_stopping, lr_scheduler], verbose=0)
|
||||
|
||||
best_models[sequence_length] = {
|
||||
'model': best_model,
|
||||
'best_hyperparameters': {
|
||||
'units': best_hps.get('units'),
|
||||
'dropout_rate': best_hps.get('dropout_rate'),
|
||||
'learning_rate': best_hps.get('learning_rate')
|
||||
}
|
||||
}
|
||||
|
||||
return best_models
|
||||
|
||||
# === Training & Validation ===
|
||||
def train_models_v2(user_data, user_data_val, sequence_length, model_type):
|
||||
tuner_dir = "./working/tuner/"+model_type
|
||||
#val_metric = 'val_f1'
|
||||
val_metric = 'val_precision'
|
||||
|
||||
early_stopping = EarlyStopping(monitor=val_metric, patience=3, restore_best_weights=True)
|
||||
lr_scheduler = ReduceLROnPlateau(monitor=val_metric, factor=0.5, patience=2)
|
||||
|
||||
shutil.rmtree(tuner_dir, ignore_errors=True)
|
||||
|
||||
x, y = prepare_data_for_model(user_data=user_data, sequence_length=sequence_length)
|
||||
x_val, y_val = prepare_data_for_model(user_data=user_data_val, sequence_length=sequence_length)
|
||||
|
||||
n_features = x.shape[2]
|
||||
users = list(user_data.keys())
|
||||
|
||||
#y_val = np.array(y_val).reshape(-1, 1)
|
||||
#y = np.array(y).reshape(-1, 1)
|
||||
|
||||
def build_model(hp):
|
||||
units_hp = hp.Int('units', 2, 8, step=2, sampling="log")
|
||||
# units_hp = hp.Int('units', 2, 256, step=2, sampling="log")
|
||||
|
||||
model = Sequential()
|
||||
model.add(Input((sequence_length, n_features)))
|
||||
if model_type==model_type_bilstm:
|
||||
model.add(Bidirectional(LSTM(units=units_hp)))
|
||||
if model_type==model_type_lstm:
|
||||
model.add(LSTM(units=units_hp))
|
||||
if model_type==model_type_gru:
|
||||
model.add(GRU(units=units_hp))
|
||||
model.add(Dropout(hp.Float('dropout_rate', 0.1, 0.2, step=0.1)))
|
||||
model.add(Dense(len(users), activation='softmax'))
|
||||
model.compile(
|
||||
optimizer=Adam(learning_rate=hp.Choice('learning_rate', [1e-5])),
|
||||
loss='sparse_categorical_crossentropy',
|
||||
metrics=[#F1Score(name='f1', average='weighted'),
|
||||
Precision(), #Recall(), Accuracy()
|
||||
]
|
||||
)
|
||||
return model
|
||||
|
||||
tuner = RandomSearch(
|
||||
build_model,
|
||||
objective=keras_tuner.Objective(val_metric, direction="max"),
|
||||
max_trials=120,
|
||||
directory=tuner_dir,
|
||||
)
|
||||
|
||||
tuner.search(x, y, epochs=epochs, validation_data=(x_val, y_val),
|
||||
callbacks=[early_stopping, lr_scheduler])
|
||||
return tuner.get_best_models(num_models=1)[0]
|
||||
|
||||
|
||||
def train_one_model(train_data, val_data, n_batch, n_epochs, n_neurons,n_neurons2,n_neurons3,n_neurons4, l_rate, d1, r1, reg1, r2, reg2, sequence_length, model_type):
|
||||
x, y = prepare_data_for_model(user_data=train_data, sequence_length=sequence_length)
|
||||
n_features = x.shape[2]
|
||||
users = list(train_data.keys())
|
||||
|
||||
# prepare model
|
||||
def build_model():
|
||||
model = Sequential()
|
||||
model.add(Input(shape=(sequence_length, n_features), batch_size=n_batch))
|
||||
if model_type == model_type_bilstm:
|
||||
model.add(Bidirectional(LSTM(n_neurons)))
|
||||
if model_type == model_type_lstm:
|
||||
# model.add(LSTM(n_neurons, kernel_regularizer=reg1, return_sequences=True))
|
||||
model.add(LSTM(n_neurons))
|
||||
# model.add(LSTM(n_neurons2))
|
||||
if model_type == model_type_gru:
|
||||
model.add(GRU(n_neurons))
|
||||
#model.add(Dense(n_neurons, activation='relu'))
|
||||
#model.add(Dropout(d1))
|
||||
model.add(Dense(len(users), activation='softmax'))
|
||||
model.compile(
|
||||
optimizer=Adam(learning_rate=l_rate),
|
||||
loss=SparseCategoricalCrossentropy(),
|
||||
metrics=[SparseCategoricalAccuracy()],
|
||||
)
|
||||
return model
|
||||
|
||||
model = build_model()
|
||||
|
||||
# fit model
|
||||
train_acc, test_acc, train_p, test_p, train_r, test_r, train_f1, test_f1 = list(), list(),list(), list(),list(), list(),list(), list()
|
||||
for i in range(n_epochs):
|
||||
model.fit(x, y, batch_size=n_batch, epochs=1, verbose=0, shuffle=False)
|
||||
# evaluate model on train data
|
||||
acc, p, r, f1 = evaluate(model, train_data, sequence_length, n_batch)
|
||||
train_acc.append(acc)
|
||||
train_p.append(p)
|
||||
train_r.append(r)
|
||||
train_f1.append(f1)
|
||||
# evaluate model on test data
|
||||
savename = 'cf_matrix_'+get_save_id(n_epochs, n_neurons, n_neurons2,n_neurons3, n_neurons4, n_batch, l_rate,d1,r1, r2)+'.json'
|
||||
acc, p, r, f1 = evaluate(model, val_data, sequence_length, n_batch, save_name=savename)
|
||||
test_acc.append(acc)
|
||||
test_p.append(p)
|
||||
test_r.append(r)
|
||||
test_f1.append(f1)
|
||||
|
||||
history = DataFrame()
|
||||
history['train_acc'], history['test_acc'] = train_acc, test_acc
|
||||
history['train_p'], history['test_p'] = train_p, test_p
|
||||
history['train_r'], history['test_r'] = train_r, test_r
|
||||
history['train_f1'], history['test_f1'] = train_f1, test_f1
|
||||
return history
|
||||
|
||||
|
||||
def train_one_model_v2(train_data, val_data, n_batch, n_epochs, n_neurons, sequence_length, model_type):
|
||||
x, y = train_data
|
||||
x_v, y_v = val_data
|
||||
users = list(set(y))
|
||||
|
||||
# renumber users
|
||||
user_map = {users[i]:i for i in range(len(users))}
|
||||
y = np.array([user_map[x] for x in y])
|
||||
y_v = np.array([user_map[x] for x in y_v])
|
||||
n_features = x.shape[2]
|
||||
user_num = len(users)
|
||||
|
||||
# prepare model
|
||||
def build_model():
|
||||
model = Sequential()
|
||||
model.add(Input(shape=(sequence_length, n_features), batch_size=n_batch))
|
||||
if model_type == model_type_bilstm:
|
||||
model.add(Bidirectional(LSTM(n_neurons)))
|
||||
if model_type == model_type_lstm:
|
||||
model.add(LSTM(n_neurons))
|
||||
if model_type == model_type_gru:
|
||||
model.add(GRU(n_neurons))
|
||||
#model.add(Dense(n_neurons, activation='relu'))
|
||||
#model.add(Dropout(d1))
|
||||
model.add(Dense(user_num, activation='softmax'))
|
||||
model.compile(
|
||||
optimizer=Adam(),
|
||||
loss=SparseCategoricalCrossentropy(),
|
||||
metrics=[SparseCategoricalAccuracy()],
|
||||
)
|
||||
return model
|
||||
|
||||
model = build_model()
|
||||
|
||||
# fit model
|
||||
train_acc, test_acc, train_p, test_p, train_r, test_r, train_f1, test_f1 = list(), list(),list(), list(),list(), list(),list(), list()
|
||||
for i in range(n_epochs):
|
||||
model.fit(x, y, batch_size=n_batch, epochs=1, verbose=0, shuffle=False)
|
||||
# evaluate model on train data
|
||||
acc, p, r, f1 = evaluate_v2(model, (x,y), sequence_length, n_batch)
|
||||
train_acc.append(acc)
|
||||
train_p.append(p)
|
||||
train_r.append(r)
|
||||
train_f1.append(f1)
|
||||
# evaluate model on test data
|
||||
savename = 'cf_matrix_'+get_save_id(n_epochs, n_neurons, '', '', '', n_batch, '','','','')+'.json'
|
||||
acc, p, r, f1 = evaluate_v2(model, (x_v, y_v), sequence_length, n_batch, save_name=savename)
|
||||
test_acc.append(acc)
|
||||
test_p.append(p)
|
||||
test_r.append(r)
|
||||
test_f1.append(f1)
|
||||
|
||||
history = DataFrame()
|
||||
history['train_acc'], history['test_acc'] = train_acc, test_acc
|
||||
history['train_p'], history['test_p'] = train_p, test_p
|
||||
history['train_r'], history['test_r'] = train_r, test_r
|
||||
history['train_f1'], history['test_f1'] = train_f1, test_f1
|
||||
return history
|
||||
|
||||
def get_save_id(n_epochs, n_neurons, n_neurons2,n_neurons3,n_neurons4, n_batch, l_rate, d1,r1, r2):
|
||||
return '_e'+str(n_epochs)+'_n'+str(n_neurons)+'_b'+ str(n_batch)
|
||||
#'x'+str(n_neurons3)+'x'+str(n_neurons4)
|
||||
#+'_l'+str(l_rate)+'_r'+str(r1)+'xx'+str(r2)
|
||||
|
||||
|
||||
def evaluate(model, df, sequence_length, batch_size, save_name=None):
|
||||
x, y = prepare_data_for_model(user_data=df, sequence_length=sequence_length)
|
||||
x = np.array(x)
|
||||
y_true = np.array(y)
|
||||
|
||||
y_pred = model.predict(x, verbose=0, batch_size=batch_size)
|
||||
y_pred_classes = np.argmax(y_pred, axis=1)
|
||||
cf_matrix = pd.DataFrame(confusion_matrix(y_true, y_pred_classes))
|
||||
if save_name is not None:
|
||||
cf_matrix.to_json('results/'+save_name)
|
||||
true_counts = pd.DataFrame(y).value_counts()
|
||||
print('Top true occurrences', true_counts[:6])
|
||||
predicted_counts = pd.DataFrame(y_pred_classes).value_counts()
|
||||
print('Top predicted occurrences', predicted_counts[:6])
|
||||
|
||||
return eval_metrics(y_true=y_true, y_pred=y_pred_classes)
|
||||
|
||||
def evaluate_v2(model, data, sequence_length, batch_size, save_name=None):
|
||||
x, y_true = data
|
||||
y_pred = model.predict(x, verbose=0, batch_size=batch_size)
|
||||
y_pred_classes = np.argmax(y_pred, axis=1)
|
||||
cf_matrix = pd.DataFrame(confusion_matrix(y_true, y_pred_classes))
|
||||
if save_name is not None:
|
||||
cf_matrix.to_json('results/'+save_name)
|
||||
true_counts = pd.DataFrame(y_true).value_counts()
|
||||
print('Top true occurrences', true_counts[:6])
|
||||
predicted_counts = pd.DataFrame(y_pred_classes).value_counts()
|
||||
print('Top predicted occurrences', predicted_counts[:6])
|
||||
|
||||
return eval_metrics(y_true=y_true, y_pred=y_pred_classes)
|
||||
|
||||
|
||||
def eval_metrics(y_true, y_pred):
|
||||
f1 = f1_score(y_true=y_true, y_pred=y_pred, average='weighted')
|
||||
p = precision_score(y_true=y_true, y_pred=y_pred, average='weighted')
|
||||
r = recall_score(y_true=y_true, y_pred=y_pred, average='weighted')
|
||||
acc = accuracy_score(y_true=y_true, y_pred=y_pred)
|
||||
return acc, p, r, f1
|
||||
|
||||
# === Evaluation ===
|
||||
def evaluate_models(best_models, df_test, sequence_lengths, output_excel_path, ALLUSERS32_15MIN_WITHOUTTHREHOLD):
|
||||
print("\n🧪 Evaluating on Test Data...")
|
||||
with ExcelWriter(output_excel_path) as writer:
|
||||
for sequence_length in sequence_lengths:
|
||||
if sequence_length not in best_models:
|
||||
continue
|
||||
evaluate_model_on_test_data(best_models[sequence_length]['model'], df_test.copy(),
|
||||
sequence_length, writer, ALLUSERS32_15MIN_WITHOUTTHREHOLD)
|
||||
|
||||
def evaluate_model_on_test_data(model, test_df, sequence_length, excel_writer, ALLUSERS32_15MIN_WITHOUTTHREHOLD):
|
||||
if(ALLUSERS32_15MIN_WITHOUTTHREHOLD):
|
||||
test_df = test_df.drop(columns=['Month', 'Year', 'date', 'DayOfWeek'])
|
||||
else:
|
||||
test_df = test_df.drop(columns=['Month', 'Year', 'date'])
|
||||
|
||||
test_df = test_df.sort_values(by='user').reset_index(drop=True)
|
||||
|
||||
users = test_df['user'].unique()
|
||||
results = []
|
||||
accuracy_above_50 = 0
|
||||
|
||||
for user in users:
|
||||
user_df = test_df[test_df['user'] == user]
|
||||
X, y_true = [], []
|
||||
user_features = user_df.drop(columns=['user']).values
|
||||
user_labels = user_df['user'].values
|
||||
|
||||
if len(user_df) <= sequence_length:
|
||||
print(f"Skipping User {user} (not enough data for sequence length {sequence_length})")
|
||||
continue
|
||||
|
||||
for i in range(len(user_df) - sequence_length):
|
||||
seq_x = user_features[i:i + sequence_length]
|
||||
seq_y = user_labels[i + sequence_length]
|
||||
X.append(seq_x)
|
||||
y_true.append(seq_y)
|
||||
|
||||
X = np.array(X)
|
||||
y_true = np.array(y_true)
|
||||
|
||||
if len(X) == 0:
|
||||
continue
|
||||
|
||||
y_pred = model.predict(X, verbose=0)
|
||||
y_pred_classes = np.argmax(y_pred, axis=1)
|
||||
|
||||
# counts which class was predicted how often
|
||||
unique_pred, counts_pred = np.unique(y_pred_classes, return_counts=True)
|
||||
label_counts_pred = dict(zip(unique_pred, counts_pred))
|
||||
|
||||
# counts which class should have been predicted how often (only one class for the user)
|
||||
unique_true, counts_true = np.unique(y_true, return_counts=True)
|
||||
label_counts_true = dict(zip(unique_true, counts_true))
|
||||
|
||||
# the fraction of correctly classified samples
|
||||
acc = accuracy_score(y_true, y_pred_classes)
|
||||
if acc > 0.5:
|
||||
accuracy_above_50 += 1
|
||||
|
||||
results.append({
|
||||
'User': user,
|
||||
'Accuracy (%)': acc * 100,
|
||||
'Predicted Class Distribution': str(label_counts_pred),
|
||||
'Actual Class Distribution': str(label_counts_true)
|
||||
})
|
||||
|
||||
print(f"\n=== User {user} ===")
|
||||
print(f"✅ Accuracy: {acc * 100:.2f}%")
|
||||
print("📊 Predicted Class Distribution:", label_counts_pred)
|
||||
print("📌 Actual Class Distribution: ", label_counts_true)
|
||||
|
||||
final_accuracy_percent = (accuracy_above_50 / 32) * 100
|
||||
print(f"\n🟩 Final Evaluation Summary for Sequence Length {sequence_length}:")
|
||||
print(f"Users with >50% Accuracy: {accuracy_above_50} / 32")
|
||||
print(f"✅ Final Success Rate: {final_accuracy_percent:.2f}%")
|
||||
|
||||
results.append({
|
||||
'User': 'TOTAL',
|
||||
'Accuracy (%)': '',
|
||||
'Predicted Class Distribution': f'Users >50% Acc: {accuracy_above_50}/32',
|
||||
'Actual Class Distribution': f'Success Rate: {final_accuracy_percent:.2f}%'
|
||||
})
|
||||
|
||||
df_results = pd.DataFrame(results)
|
||||
df_results.to_excel(excel_writer, sheet_name=f"SeqLen_{sequence_length}", index=False)
|
||||
@@ -37,8 +37,7 @@ scipy==1.16.0
|
||||
six==1.17.0
|
||||
tensorboard==2.19.0
|
||||
tensorboard-data-server==0.7.2
|
||||
tensorflow==2.19.0
|
||||
tensorflow-io-gcs-filesystem==0.31.0
|
||||
tensorflow==2.20.0
|
||||
termcolor==3.1.0
|
||||
threadpoolctl==3.6.0
|
||||
typing_extensions==4.14.1
|
||||
+70
-286
@@ -1,19 +1,15 @@
|
||||
import random
|
||||
|
||||
import keras_tuner
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import shutil
|
||||
|
||||
from keras import Input
|
||||
from keras.src.losses import SparseCategoricalCrossentropy
|
||||
from keras.src.metrics import F1Score, Precision, Recall, Accuracy, SparseCategoricalAccuracy
|
||||
from pandas import ExcelWriter, DataFrame
|
||||
from keras.src.metrics import SparseCategoricalAccuracy
|
||||
from pandas import DataFrame
|
||||
from tensorflow.keras.models import Sequential
|
||||
from tensorflow.keras.layers import LSTM, Dense, Dropout, Bidirectional,GRU
|
||||
from tensorflow.keras.layers import LSTM, Dense, Bidirectional,GRU
|
||||
from tensorflow.keras.optimizers import Adam
|
||||
from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping
|
||||
from keras_tuner import RandomSearch
|
||||
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix
|
||||
|
||||
epochs = 5#50
|
||||
@@ -21,61 +17,49 @@ model_type_gru = 'GRU'
|
||||
model_type_lstm = 'LSTM'
|
||||
model_type_bilstm = 'BiLSTM'
|
||||
|
||||
|
||||
# === Display functions ===
|
||||
def display_warning_about_2020_data():
|
||||
print("\n⚠️ Warning: 2020 data after February is excluded due to COVID-19.")
|
||||
print("✅ Only Jan and Feb 2020 are used for testing. Do not use them in training/validation.")
|
||||
|
||||
def display_warnings_for_scenarios(scenario_type, predefined_training_scenarios, predefined_validation_scenarios):
|
||||
if scenario_type == "training":
|
||||
print("\n⚠️ Predefined Training Scenarios (for reference only):")
|
||||
for name, scenario in predefined_training_scenarios.items():
|
||||
parts = [f"{year}-{months}" for year, months in scenario['years_months']]
|
||||
print(f" {name}: {', '.join(parts)}")
|
||||
elif scenario_type == "validation":
|
||||
print("\n⚠️ Predefined Validation Scenario:")
|
||||
for name, scenario in predefined_validation_scenarios.items():
|
||||
parts = [f"{year}-{months}" for year, months in scenario['years_months']]
|
||||
print(f" {name}: {', '.join(parts)}")
|
||||
|
||||
# === Data functions ===
|
||||
def load_dataset(file_path):
|
||||
return pd.read_excel(file_path)
|
||||
|
||||
def filter_data(df, scenario, ALLUSERS32_15MIN_WITHOUTREHOLD):
|
||||
filtered = pd.DataFrame()
|
||||
for year, months in scenario:
|
||||
filtered = pd.concat([filtered, df[(df['Year'] == year) & (df['Month'].isin(months))]])
|
||||
|
||||
if ALLUSERS32_15MIN_WITHOUTREHOLD:
|
||||
return filtered.drop(columns=['Month', 'Year', 'date', 'DayOfWeek'])
|
||||
else:
|
||||
return filtered.drop(columns=['Month', 'Year', 'date'])
|
||||
|
||||
def filter_test_data(df, scenario):
|
||||
data_parts = []
|
||||
for year, months in scenario:
|
||||
part = df[(df['Year'] == year) & (df['Month'].isin(months))]
|
||||
data_parts.append(part)
|
||||
return pd.concat(data_parts, ignore_index=True)
|
||||
|
||||
def prepare_user_data(df):
|
||||
#df_sorted = df.sort_values(by='user').reset_index(drop=True)
|
||||
users = df['user'].unique()
|
||||
return {user: df[df['user'] == user] for user in users}
|
||||
|
||||
def make_sequences(data, sequence_length):
|
||||
"""
|
||||
Converts the data into sequences of the given length
|
||||
|
||||
:param data: Original data
|
||||
:param sequence_length: length of intended sequences
|
||||
:return: x,y for the sequences
|
||||
"""
|
||||
x, y = [], []
|
||||
features = data.drop('user', axis=1).values
|
||||
labels = data['user'].values
|
||||
# for i in range(len(features) - sequence_length+1): # with overlap on days
|
||||
for i in range(0, len(features) - sequence_length + 1, sequence_length): # without overlap on days
|
||||
for i in range(len(features) - sequence_length+1): # with overlap on days
|
||||
# for i in range(0, len(features) - sequence_length + 1, sequence_length): # without overlap on days
|
||||
x.append(features[i:i + sequence_length])
|
||||
y.append(labels[i + sequence_length-1])
|
||||
return x, y
|
||||
|
||||
def prepare_data_for_model(user_data, sequence_length, print_counts=False):
|
||||
def prepare_data_for_basic_algorithm(user_data, sequence_length):
|
||||
"""
|
||||
Converts the data into a format the sklearn algorithms can work with. Does not change the data, only the structure
|
||||
:param user_data: the dict of dataframe with the data
|
||||
:param sequence_length: intended sequence length
|
||||
:return: the formatted data
|
||||
"""
|
||||
combined = pd.DataFrame()
|
||||
for user, data in user_data.items():
|
||||
x_new, y_new = make_sequences(data, sequence_length)
|
||||
if len(x_new)>0:
|
||||
var = [[pd.DataFrame(a[s]) for s in range(sequence_length)] for a in x_new]
|
||||
df_var = pd.concat([pd.concat(seq_list).T for seq_list in var])
|
||||
df_var['user'] = user
|
||||
combined = pd.concat([combined, df_var], ignore_index=True)
|
||||
return combined.drop(columns=['user']), combined['user']
|
||||
|
||||
def prepare_data_for_neural_model(user_data, sequence_length, print_counts=False):
|
||||
"""
|
||||
Converts the data into a format the neural model can work with. Does not change the data, only the structure
|
||||
|
||||
:param print_counts: Whether to print some additional debug data
|
||||
:param user_data: the dict of dataframe with the data
|
||||
:param sequence_length: intended sequence length
|
||||
:return: the formatted data
|
||||
"""
|
||||
x, y = [], []
|
||||
combined = pd.DataFrame()
|
||||
for user, data in user_data.items():
|
||||
@@ -102,133 +86,17 @@ def prepare_data_for_model(user_data, sequence_length, print_counts=False):
|
||||
y = np.array(y)
|
||||
return x,y
|
||||
|
||||
# === Training & Validation ===
|
||||
def train_models(user_data, user_data_val, sequence_lengths, tuner_dir="./working/tuner", model_type=model_type_lstm):
|
||||
best_models = {}
|
||||
early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)
|
||||
lr_scheduler = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=5, verbose=1)
|
||||
users = list(user_data.keys())
|
||||
|
||||
shutil.rmtree(tuner_dir, ignore_errors=True)
|
||||
|
||||
for sequence_length in sequence_lengths:
|
||||
print(f"\n=== Training for Sequence Length: {sequence_length} ===")
|
||||
X, y = prepare_data_for_model(user_data=user_data, sequence_length=sequence_length)
|
||||
X_val, y_val = prepare_data_for_model(user_data=user_data_val, sequence_length=sequence_length)
|
||||
|
||||
if X.shape[0] == 0 or X_val.shape[0] == 0:
|
||||
print(f"⚠️ Skipped sequence length {sequence_length} due to insufficient data.")
|
||||
continue
|
||||
|
||||
n_features = X.shape[2]
|
||||
|
||||
def build_model(hp):
|
||||
model = Sequential()
|
||||
if model_type==model_type_bilstm:
|
||||
model.add(Bidirectional(LSTM(units=hp.Int('units', 32, 256, step=2),
|
||||
input_shape=(sequence_length, n_features))))
|
||||
if model_type==model_type_lstm:
|
||||
model.add(LSTM(units=hp.Int('units', 32, 256, step=2),
|
||||
input_shape=(sequence_length, n_features)))
|
||||
if model_type==model_type_gru:
|
||||
model.add(GRU(units=hp.Int('units', 32, 256, step=2),
|
||||
input_shape=(sequence_length, n_features)))
|
||||
model.add(Dropout(hp.Float('dropout_rate', 0.1, 0.5, step=0.1)))
|
||||
model.add(Dense(len(users), activation='softmax'))
|
||||
model.compile(
|
||||
optimizer=Adam(learning_rate=hp.Choice('learning_rate', [1e-2, 1e-3, 1e-4])),
|
||||
loss='sparse_categorical_crossentropy',
|
||||
metrics=['accuracy']
|
||||
)
|
||||
return model
|
||||
|
||||
tuner = RandomSearch(
|
||||
build_model,
|
||||
objective='val_loss',
|
||||
max_trials=30,
|
||||
executions_per_trial=2,
|
||||
directory=tuner_dir,
|
||||
project_name=f'lstm_seq_{sequence_length}'
|
||||
)
|
||||
|
||||
tuner.search(X, y, epochs=epochs, validation_data=(X_val, y_val),
|
||||
callbacks=[early_stopping, lr_scheduler], verbose=0)
|
||||
|
||||
best_hps = tuner.get_best_hyperparameters(1)[0]
|
||||
best_model = tuner.hypermodel.build(best_hps)
|
||||
best_model.fit(X, y, epochs=epochs, validation_data=(X_val, y_val),
|
||||
callbacks=[early_stopping, lr_scheduler], verbose=0)
|
||||
|
||||
best_models[sequence_length] = {
|
||||
'model': best_model,
|
||||
'best_hyperparameters': {
|
||||
'units': best_hps.get('units'),
|
||||
'dropout_rate': best_hps.get('dropout_rate'),
|
||||
'learning_rate': best_hps.get('learning_rate')
|
||||
}
|
||||
}
|
||||
|
||||
return best_models
|
||||
|
||||
# === Training & Validation ===
|
||||
def train_models_v2(user_data, user_data_val, sequence_length, model_type):
|
||||
tuner_dir = "./working/tuner/"+model_type
|
||||
#val_metric = 'val_f1'
|
||||
val_metric = 'val_precision'
|
||||
|
||||
early_stopping = EarlyStopping(monitor=val_metric, patience=3, restore_best_weights=True)
|
||||
lr_scheduler = ReduceLROnPlateau(monitor=val_metric, factor=0.5, patience=2)
|
||||
|
||||
shutil.rmtree(tuner_dir, ignore_errors=True)
|
||||
|
||||
x, y = prepare_data_for_model(user_data=user_data, sequence_length=sequence_length)
|
||||
x_val, y_val = prepare_data_for_model(user_data=user_data_val, sequence_length=sequence_length)
|
||||
def train_one_model(train_data, val_data, n_batch, n_epochs, n_neurons, sequence_length, model_type):
|
||||
x, y = train_data
|
||||
x_v, y_v = val_data
|
||||
users = list(set(y))
|
||||
|
||||
# renumber users
|
||||
user_map = {users[i]:i for i in range(len(users))}
|
||||
y = np.array([user_map[x] for x in y])
|
||||
y_v = np.array([user_map[x] for x in y_v])
|
||||
n_features = x.shape[2]
|
||||
users = list(user_data.keys())
|
||||
|
||||
#y_val = np.array(y_val).reshape(-1, 1)
|
||||
#y = np.array(y).reshape(-1, 1)
|
||||
|
||||
def build_model(hp):
|
||||
units_hp = hp.Int('units', 2, 8, step=2, sampling="log")
|
||||
# units_hp = hp.Int('units', 2, 256, step=2, sampling="log")
|
||||
|
||||
model = Sequential()
|
||||
model.add(Input((sequence_length, n_features)))
|
||||
if model_type==model_type_bilstm:
|
||||
model.add(Bidirectional(LSTM(units=units_hp)))
|
||||
if model_type==model_type_lstm:
|
||||
model.add(LSTM(units=units_hp))
|
||||
if model_type==model_type_gru:
|
||||
model.add(GRU(units=units_hp))
|
||||
model.add(Dropout(hp.Float('dropout_rate', 0.1, 0.2, step=0.1)))
|
||||
model.add(Dense(len(users), activation='softmax'))
|
||||
model.compile(
|
||||
optimizer=Adam(learning_rate=hp.Choice('learning_rate', [1e-5])),
|
||||
loss='sparse_categorical_crossentropy',
|
||||
metrics=[#F1Score(name='f1', average='weighted'),
|
||||
Precision(), #Recall(), Accuracy()
|
||||
]
|
||||
)
|
||||
return model
|
||||
|
||||
tuner = RandomSearch(
|
||||
build_model,
|
||||
objective=keras_tuner.Objective(val_metric, direction="max"),
|
||||
max_trials=120,
|
||||
directory=tuner_dir,
|
||||
)
|
||||
|
||||
tuner.search(x, y, epochs=epochs, validation_data=(x_val, y_val),
|
||||
callbacks=[early_stopping, lr_scheduler])
|
||||
return tuner.get_best_models(num_models=1)[0]
|
||||
|
||||
|
||||
def train_one_model(train_data, val_data, n_batch, n_epochs, n_neurons,n_neurons2,n_neurons3,n_neurons4, l_rate, d1, r1, reg1, r2, reg2, sequence_length, model_type):
|
||||
x, y = prepare_data_for_model(user_data=train_data, sequence_length=sequence_length)
|
||||
n_features = x.shape[2]
|
||||
users = list(train_data.keys())
|
||||
user_num = len(users)
|
||||
|
||||
# prepare model
|
||||
def build_model():
|
||||
@@ -237,16 +105,12 @@ def train_one_model(train_data, val_data, n_batch, n_epochs, n_neurons,n_neurons
|
||||
if model_type == model_type_bilstm:
|
||||
model.add(Bidirectional(LSTM(n_neurons)))
|
||||
if model_type == model_type_lstm:
|
||||
# model.add(LSTM(n_neurons, kernel_regularizer=reg1, return_sequences=True))
|
||||
model.add(LSTM(n_neurons))
|
||||
# model.add(LSTM(n_neurons2))
|
||||
if model_type == model_type_gru:
|
||||
model.add(GRU(n_neurons))
|
||||
#model.add(Dense(n_neurons, activation='relu'))
|
||||
#model.add(Dropout(d1))
|
||||
model.add(Dense(len(users), activation='softmax'))
|
||||
model.add(Dense(user_num, activation='softmax'))
|
||||
model.compile(
|
||||
optimizer=Adam(learning_rate=l_rate),
|
||||
optimizer=Adam(),
|
||||
loss=SparseCategoricalCrossentropy(),
|
||||
metrics=[SparseCategoricalAccuracy()],
|
||||
)
|
||||
@@ -259,14 +123,14 @@ def train_one_model(train_data, val_data, n_batch, n_epochs, n_neurons,n_neurons
|
||||
for i in range(n_epochs):
|
||||
model.fit(x, y, batch_size=n_batch, epochs=1, verbose=0, shuffle=False)
|
||||
# evaluate model on train data
|
||||
acc, p, r, f1 = evaluate(model, train_data, sequence_length, n_batch)
|
||||
acc, p, r, f1 = evaluate(model, (x, y), sequence_length, n_batch)
|
||||
train_acc.append(acc)
|
||||
train_p.append(p)
|
||||
train_r.append(r)
|
||||
train_f1.append(f1)
|
||||
# evaluate model on test data
|
||||
savename = 'cf_matrix_'+get_save_id(n_epochs, n_neurons, n_neurons2,n_neurons3, n_neurons4, n_batch, l_rate,d1,r1, r2)+'.json'
|
||||
acc, p, r, f1 = evaluate(model, val_data, sequence_length, n_batch, save_name=savename)
|
||||
savename = 'cf_matrix_'+get_save_id(n_epochs, n_neurons, n_batch)+'.json'
|
||||
acc, p, r, f1 = evaluate(model, (x_v, y_v), n_batch, save_name=savename)
|
||||
test_acc.append(acc)
|
||||
test_p.append(p)
|
||||
test_r.append(r)
|
||||
@@ -279,24 +143,26 @@ def train_one_model(train_data, val_data, n_batch, n_epochs, n_neurons,n_neurons
|
||||
history['train_f1'], history['test_f1'] = train_f1, test_f1
|
||||
return history
|
||||
|
||||
|
||||
def get_save_id(n_epochs, n_neurons, n_neurons2,n_neurons3,n_neurons4, n_batch, l_rate, d1,r1, r2):
|
||||
def get_save_id(n_epochs, n_neurons, n_batch):
|
||||
return '_e'+str(n_epochs)+'_n'+str(n_neurons)+'_b'+ str(n_batch)
|
||||
#'x'+str(n_neurons3)+'x'+str(n_neurons4)
|
||||
#+'_l'+str(l_rate)+'_r'+str(r1)+'xx'+str(r2)
|
||||
|
||||
def evaluate(model, data, batch_size, save_name=None):
|
||||
"""
|
||||
GIven a model, the data is used for prediction and then evaluated.
|
||||
|
||||
def evaluate(model, df, sequence_length, batch_size, save_name=None):
|
||||
x, y = prepare_data_for_model(user_data=df, sequence_length=sequence_length)
|
||||
x = np.array(x)
|
||||
y_true = np.array(y)
|
||||
|
||||
:param model: Model to use with a .predict() call
|
||||
:param data: x, y_true of the data, already prepared for the model
|
||||
:param batch_size: batch size for prediction
|
||||
:param save_name: if provided, results will be saved to a json file of that name
|
||||
:return: the evaluation results
|
||||
"""
|
||||
x, y_true = data
|
||||
y_pred = model.predict(x, verbose=0, batch_size=batch_size)
|
||||
y_pred_classes = np.argmax(y_pred, axis=1)
|
||||
cf_matrix = pd.DataFrame(confusion_matrix(y_true, y_pred_classes))
|
||||
if save_name is not None:
|
||||
cf_matrix.to_json('results/'+save_name)
|
||||
true_counts = pd.DataFrame(y).value_counts()
|
||||
true_counts = pd.DataFrame(y_true).value_counts()
|
||||
print('Top true occurrences', true_counts[:6])
|
||||
predicted_counts = pd.DataFrame(y_pred_classes).value_counts()
|
||||
print('Top predicted occurrences', predicted_counts[:6])
|
||||
@@ -304,97 +170,15 @@ def evaluate(model, df, sequence_length, batch_size, save_name=None):
|
||||
return eval_metrics(y_true=y_true, y_pred=y_pred_classes)
|
||||
|
||||
|
||||
|
||||
def eval_metrics(y_true, y_pred):
|
||||
"""
|
||||
Calculate the evaluation metrics
|
||||
:param y_true:
|
||||
:param y_pred:
|
||||
:return: acc, p, r, f1
|
||||
"""
|
||||
f1 = f1_score(y_true=y_true, y_pred=y_pred, average='weighted')
|
||||
p = precision_score(y_true=y_true, y_pred=y_pred, average='weighted')
|
||||
r = recall_score(y_true=y_true, y_pred=y_pred, average='weighted')
|
||||
acc = accuracy_score(y_true=y_true, y_pred=y_pred)
|
||||
return acc, p, r, f1
|
||||
|
||||
# === Evaluation ===
|
||||
def evaluate_models(best_models, df_test, sequence_lengths, output_excel_path, ALLUSERS32_15MIN_WITHOUTTHREHOLD):
|
||||
print("\n🧪 Evaluating on Test Data...")
|
||||
with ExcelWriter(output_excel_path) as writer:
|
||||
for sequence_length in sequence_lengths:
|
||||
if sequence_length not in best_models:
|
||||
continue
|
||||
evaluate_model_on_test_data(best_models[sequence_length]['model'], df_test.copy(),
|
||||
sequence_length, writer, ALLUSERS32_15MIN_WITHOUTTHREHOLD)
|
||||
|
||||
def evaluate_model_on_test_data(model, test_df, sequence_length, excel_writer, ALLUSERS32_15MIN_WITHOUTTHREHOLD):
|
||||
if(ALLUSERS32_15MIN_WITHOUTTHREHOLD):
|
||||
test_df = test_df.drop(columns=['Month', 'Year', 'date', 'DayOfWeek'])
|
||||
else:
|
||||
test_df = test_df.drop(columns=['Month', 'Year', 'date'])
|
||||
|
||||
test_df = test_df.sort_values(by='user').reset_index(drop=True)
|
||||
|
||||
users = test_df['user'].unique()
|
||||
results = []
|
||||
accuracy_above_50 = 0
|
||||
|
||||
for user in users:
|
||||
user_df = test_df[test_df['user'] == user]
|
||||
X, y_true = [], []
|
||||
user_features = user_df.drop(columns=['user']).values
|
||||
user_labels = user_df['user'].values
|
||||
|
||||
if len(user_df) <= sequence_length:
|
||||
print(f"Skipping User {user} (not enough data for sequence length {sequence_length})")
|
||||
continue
|
||||
|
||||
for i in range(len(user_df) - sequence_length):
|
||||
seq_x = user_features[i:i + sequence_length]
|
||||
seq_y = user_labels[i + sequence_length]
|
||||
X.append(seq_x)
|
||||
y_true.append(seq_y)
|
||||
|
||||
X = np.array(X)
|
||||
y_true = np.array(y_true)
|
||||
|
||||
if len(X) == 0:
|
||||
continue
|
||||
|
||||
y_pred = model.predict(X, verbose=0)
|
||||
y_pred_classes = np.argmax(y_pred, axis=1)
|
||||
|
||||
# counts which class was predicted how often
|
||||
unique_pred, counts_pred = np.unique(y_pred_classes, return_counts=True)
|
||||
label_counts_pred = dict(zip(unique_pred, counts_pred))
|
||||
|
||||
# counts which class should have been predicted how often (only one class for the user)
|
||||
unique_true, counts_true = np.unique(y_true, return_counts=True)
|
||||
label_counts_true = dict(zip(unique_true, counts_true))
|
||||
|
||||
# the fraction of correctly classified samples
|
||||
acc = accuracy_score(y_true, y_pred_classes)
|
||||
if acc > 0.5:
|
||||
accuracy_above_50 += 1
|
||||
|
||||
results.append({
|
||||
'User': user,
|
||||
'Accuracy (%)': acc * 100,
|
||||
'Predicted Class Distribution': str(label_counts_pred),
|
||||
'Actual Class Distribution': str(label_counts_true)
|
||||
})
|
||||
|
||||
print(f"\n=== User {user} ===")
|
||||
print(f"✅ Accuracy: {acc * 100:.2f}%")
|
||||
print("📊 Predicted Class Distribution:", label_counts_pred)
|
||||
print("📌 Actual Class Distribution: ", label_counts_true)
|
||||
|
||||
final_accuracy_percent = (accuracy_above_50 / 32) * 100
|
||||
print(f"\n🟩 Final Evaluation Summary for Sequence Length {sequence_length}:")
|
||||
print(f"Users with >50% Accuracy: {accuracy_above_50} / 32")
|
||||
print(f"✅ Final Success Rate: {final_accuracy_percent:.2f}%")
|
||||
|
||||
results.append({
|
||||
'User': 'TOTAL',
|
||||
'Accuracy (%)': '',
|
||||
'Predicted Class Distribution': f'Users >50% Acc: {accuracy_above_50}/32',
|
||||
'Actual Class Distribution': f'Success Rate: {final_accuracy_percent:.2f}%'
|
||||
})
|
||||
|
||||
df_results = pd.DataFrame(results)
|
||||
df_results.to_excel(excel_writer, sheet_name=f"SeqLen_{sequence_length}", index=False)
|
||||
|
||||
@@ -66,7 +66,6 @@ def process_file_15_min(file_path, user_label):
|
||||
# Load the dataset
|
||||
df = pd.read_csv(file_path, delimiter=';', low_memory=False)
|
||||
|
||||
# TODO: evtl. nicht nur iPhone date nutzen
|
||||
# Filter for iPhone devices
|
||||
iphone_df = df[df['device'].str.contains('iPhone', na=False)]
|
||||
|
||||
Reference in New Issue
Block a user